{"hits":{"hits":[{"links":{"bibtex":"https://inspirehep.net/api/literature/1483465?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1483465?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1483465?format=latex-us","json":"https://inspirehep.net/api/literature/1483465?format=json","json-expanded":"https://inspirehep.net/api/literature/1483465?format=json-expanded","cv":"https://inspirehep.net/api/literature/1483465?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1483465"},"created":"2016-08-27T00:00:00+00:00","id":"1483465","updated":"2023-03-09T17:29:27.175902+00:00","metadata":{"citation_count":14,"authors":[{"raw_affiliations":[{"value":"DPNC, University of Geneva, Switzerland"}],"full_name_unicode_normalized":"wu, xin","full_name":"Wu, Xin","record":{"$ref":"https://inspirehep.net/api/authors/983103"},"last_name":"Wu","ids":[{"schema":"INSPIRE BAI","value":"Xin.Wu.1"}],"signature_block":"Wx","first_name":"Xin","uuid":"05a9884e-416e-458b-948a-548f57a44246","recid":983103}],"citation_count_without_self_citations":11,"publication_info":[{"journal_volume":"ICRC2015","artid":"1192","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1241714"},"year":2016,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1213080"},"page_start":"1192","journal_title":"PoS","parent_record":{"$ref":"https://inspirehep.net/api/literature/1389798"},"cnum":"C15-07-30"}],"documents":[{"filename":"PoS(ICRC2015)1192.pdf","attachment":{"content":"P\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\n1. Introduction\n\nThe DArk Matter Particle Explorer (DAMPE)[1] is a high energy astroparticle satellite\nmission designed to detect high energy gamma rays and cosmic rays with high precision. The\nmain science objectives are 1) the search for Dark Matter decay or annihilation signatures, 2)\nthe measurement of cosmic ray flux and composition, and 3) gamma-ray astronomy. DAMPE is\nof the five scientific space science missions planned within the framework of the Strategic\nPioneer Program on Space Science[2] of the Chinese Academy of Sciences (CAS). The design,\nprototyping, production, test and qualification of the DAMPE payload has been carried out by\ncollaborating institutes1 from China, Switzerland and Italy. \n\nThe performance goal of the DAMPE detector is to measure precisely the electron and\nphoton spectrum between 5 GeV to 10 TeV, and cosmic ray flux and chemical composition\nbetween 10 GeV to above 100 TeV, with reasonably large geometrical acceptances, ~0.3 m2sr\nfor electrons/photons, and ~0.2 m2sr for cosmic rays, using the technologies of a thick total\nabsorption calorimeter, and a precise tracker with integrated photon converters. The DAMPE\ndetector, as shown in Figure 1, consists of 4 subsystems: the plastic scintillator strips detector\n(PSD), the silicon-tungsten tracker-converter (STK), the BGO imaging calorimeter (BGO), and\nthe neutron detector (NUD).\n\n                         \n\nFigure 1: Sketch of the DAMPE detector\n\nThe PSD consists of one double layer (one x and one y) of scintillating strips detector\nmade of scintillating strips of 1 cm thick, 2.8 cm wide and 82 cm long. The strips are staggered\nby 0.8 cm in a layer, thus fully covers an area of 82 cm by 82 cm. The PSD serves as anti-\ncoincidence detector for photon identification, as well as charge detector for cosmic rays. The\ndesign specification is a position resolution of 6 mm, and a charge resolution of 0.25 for Z = 1 to\n20. The STK is the main subject of this report and details will be given in following sections.\nThe calorimeter is made up of 14 layers of BGO bars in a hodoscopic arrangement. Each BGO\nbar is 2.5 cm by 2.5 cm in cross section and 60 cm in length, making it the longest BGO crystals\never produced. The bars are readout at both ends with PMTs, each PMT is readout from 3\ndynodes (2, 5, 8) to extend the dynamic range. The total thickness of the calorimeter is\nequivalent to 31 radiation lengths and 1.6 interaction lengths. An excellent electromagnetic\nenergy resolution of 1.5% above 100 GeV, and a very good hadronic energy resolution of better\nthan 40% above 800 GeV can be expected. The neutron detector (NUD) consists of 16 1 cm-\nthick boron-doped plastic scintillator plates of 19.5 cm by 19.5 cm large each read out by a\n\n1PMO-USTC-IMP-IHEP-NSSC-UNIGE-PERUGIA-BARI-LECCE\n\n2\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nPMT. The purpose of the NUD is to detect delayed neutron resulting from a hadron shower in\norder to improve the electron/proton separation power, which should be 105 overall.\n\n2. The Silicon-Tungsten Tracker\n\nSilicon micro-strip tracker is a proven space technology that is used successfully in space\nexperiments, eg. PAMELA, AGILE, Fermi and AMS. The tracker-converter concept, in which\ntungsten foils are inserted between silicon tacking layers, has been employed in AGILE and\nFermi and proved to be an excellent technique to detect high-energy photons with high\nefficiency and precision. An incoming photon converts to an electron-positron pair in the\ntungsten converter, which can then be detected by subsequent silicon layers. Multiple scattering\nof charged particles can be mitigated by spreading the tungsten to several foils, and for high-\nenergy particles (> 5 GeV), the effect is negligible for tungsten foils of a few millimeters\nthickness. The STK combines the features of the AMS tracker and the Fermi/AGILE tracker-\nconverters to perform three important tasks: charge track reconstruction, photon detection, and\ncharge measurement of cosmic rays.  The STK is made of 6 tracking planes each consists of two\nlayers of single-sided silicon strip detectors measuring the two orthogonal views perpendicular\nto the pointing direction of the apparatus. Three layers of tungsten plates of 1mm thick are\ninserted in front of tracking layer 2, 3 and 4 for photon conversion. Figure 2 shows an exploded\nview of the STK internal structure (left panel) and a picture of the Engineering and Qualification\nModel of the STK (right panel).\n\nFigure 2: Exploded view of the STK internal structure (left). A picture of the STK EQM (right). \nThe STK uses single-sided AC-coupled silicon micro-strip detectors. The sensor is 9.5 cm\n\nby 9.5 cm in size, 320 µm thick, and segmented into 768 strips with a 121 µm pitch. Only every\nother strip is readout but since analogue readout is used the position resolution is better than 80\nµm for most incident angles, thanks to the charge division of floating strips. The photon angular\nresolution is expected to be around 0.2° at 10 GeV. The high dynamic range of the analog\nreadout electronics of the STK allows to measure the charge of the incident cosmic rays with\nhigh precision. The full tracker uses 768 sensors, equivalent to a total silicon area of ~7 m2.\n\n2.1 The Silicon Sensor and the Silicon Ladder\n\nThe single-sided AC-coupled silicon strip detectors (SSSD) were manufactured by\nHamamatsu Photonics, using the same sensor geometry used by the AGILE mission, but with\nsome noticeable differences in thickness, bulk resistivity and backplane metallisation. The\nbackplane metallisation is gold instead of aluminium which improves the backplane contact.\nThe bulk resistivity is 5-8 kΩcm, which allows to operate the STK at 80V. The body capacitance\nis 0.4 pF/cm, the inter-strip capacitance is 0.85 pF/cm, and the total strip capacitance is 2.1\npF/cm. Leakage currents at 150V of the 865 sensors (768 needed for the STK) delivered have\n\n3\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nbeen measured at the manufacturer, as shown in the left panel of Figure 3. The average value is\n116 nA, well below the specification of 900 nA. \n\nFigure 3: Leakage currents of 865 silicon strip sensors measured at 150 V at the manufacturer (left) and\nthose of 192 ladders installed in the final Flight Model of the STK (right).\n\nThe basic detection unit of the STK is the “Silicon Ladder”, formed by four silicon sensors\nglued to a bias circuit (flexible PCB) then the strips wire-bonded in series to form a ladder of\nabout 40 cm long and 9.5 cm wide, shown in the left bottom panel of Figure 4. The sensor bias\ncircuit is integrated with the frontend electronics board, as well as the readout flexible cable (the\n“pigtail”), as shown in the left top panel of Figure 4. The integrated circuit, called the Tracker\nFrontend Hybrid (TFH) uses a novel approach combining flexible and rigid PCB technology,\nthus minimizing the assembly procedure and the usage of connectors. \n\nFigure 4: A STK Tracker Frontend Hybrid (TFH) (left top), a STK Silicon Ladder (left bottom), and the\ndetails of the TFH (right). \n\nThe silicon strips are read out by the 64-channel analogue low power high dynamic range\nVA140 ASIC manufactured by Gamma Medica-Ideas AS, based in the readout chip developed\nfor the AMS silicon tracker. Six VA140 chips are housed per TFH to read out the 384 readout\nchannels of a silicon ladder. In addition to the signal readout, the TFH contains also a circuit to\ndistribute bias voltage to the silicon sensors, as well as circuits for temperature sensors. The\nright panel of Figure 4 shows the circuit details of the TFH. \n\nThe 6 VA140 chips on a TFH are read out in 2 groups of 3 in parallel. The charges of the\n192 strips connected to the 3 chips in a group are read out serially through the “pig tail” of the\nTFH into an Analogue-to-Digital-Converter (ADC) on the Tracker Readout Board (TRB), where\nthey are digitized and zero suppressed. FPGAs on the TRB then perform the cluster finding and\nsend the clusters to the DAMPE DAQ system. The TRB system was designed and produced in\nIHEP, Beijing. Each TRB consists a set of 3-board (ADC Board, FPGA Board and Power\nBoard) assembly. \n\n4\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nThe assembled TFH were thermal screened according to space environmental\nrequirements in a setup specially designed for this purpose using a thermal chamber. The\nscreening included 2 power on-off cycles and 17 power-on cycles between -30°C and 60 °C; the\nfull process took 7 days. TFH passed the screening test were used for ladder production at\nUniversity of Geneva and INFN Perugia. Produced ladders were measured for the important 4-\nsensor alignment with metrology machine. Thanks to the precise custom made ladder\nproduction jigs, the alignment precision of produced ladders was about 4 µm on average, much\nbetter than the required 40 µm, as shown in Figure 5, for a sample of 192 ladders. \n\n \n\nFigure 5: Alignment error of the top (left) and bottom (right) side of 192 ladders\nThe last step of single ladder quality assurance was a 12-hour cosmic ray test and the signal to\nnoise ratio of cluster charges from minimum ionizing particles (MIPs) are checked. On average\nthe S/N is about 15, excellent for the application of the STK. All 192 ladders needed for the\nSTK Flight Model have been produced by the end of March 2015.\n\n2.2 The STK Tracker Plane and the Tracker Assembly\n\nSixteen silicon ladders, arranged in two rows of eight, are glued to the surface of a support\ntray to cover an area of about 80 cm by 80 cm, forming a silicon layer (right panel of Figure 8).\nA support tray is made of an aluminium honeycomb structure sandwiched between two Carbon\nFibre Reinforced Polymer (CFRP) face sheets of 0.6 mm thick each, forming a light but rigid\nstructure that can sustain the vibration and the acceleration of a rocket launch. Tungsten plates\nof 1 mm thick are integrated into the support trays of the second, third and forth tracker planes\nin such a way that the converter is located immediately above a tracking layer in order to ensure\ngood conversion detection efficiency. Figure 6 shows the production of a support tray with W\nplates at Composite Design Sàrl. After fabrication each tray with W was scaned by X ray at\nCERN to check the alignment of the W plates with respect to the 4 corners.\n\nFigure 6: Tungsten plates being glued in a support tray (left); Final gluing of a support tray (right).\n\n5\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nA support tray is covered in both surfaces with silicon ladders to form a tracker plane,\nexcept for the top and the bottom trays, which have only one surface covered. The gluing of\nladders on the tracker plane was carried out at the University of Geneva, with custom made jigs\non a rotating stand, two ladders at a time. Figure 7 shows the gluing of ladders to a support tray\nand the metrology measurement of a fully loaded plane. \n\nFigure 7: Gluing ladders to a support tray (Left); Metrology measurement of a completed tracker plane (right). \nOnce a plane is assembled, all ladders on the plane are measured again for noise and\n\nleakage current. The right panel of Figure 3 shows the measured leakage currents at 80 V of 192\nladders glued to the 7 planes. The excellent quality of the silicon sensors has been maintained\nthrough the ladder production and plane assembly procedures.    \n\nTracker planes are stacked on top of each other to form the full tracker. The silicon ladders\non the bottom surface of a tracker plane and those on the top surface of the plane below form an\nx-y tracking plane, the distance between the x and the y layer of a tracking plane is about 3 mm.\nThe stack of the 7 tracker planes forms 6 tracking planes that provide 12 coordinate\nmeasurements (6 x and 6 y) when a charged track traverses the STK. Figure 8 shows the\nschematic of the STK mechanical structure (left panel) and the assembly of the STK Flight\nModel in progress, with all but the top tracker plane stacked (right panel).\n\n \nFigure 8: Schematic of the STK mechanical structure (left) and the STK FM being assembled (right).\n\nTo minimize power consumption, low power components are used as much as possible.\nThe power consumption of each ladder is about 116 mW and the total for the STK is about 90W.\nThe STK uses a passive cooling systems of heat conductors: copper vias and bands on the TFH,\npyrolytic graphite sheets (PGS) that connects from TFH to copper straps, which in turn transfers\nheat to the aluminium radiators on the outside of the STK. The thermal management system of\nthe satellite uses heat pipes to keep the radiator at a constant temperature at 10°C. \n\n3. The Construction and Test of the Engineering and Qualification Model (EQM)\n\n The main design concepts of the mechanical support structure and thermal management\nhave been qualified with the EQM in a series of space environmental qualification tests,\nincluding vibration, acceleration, shock, thermal cycling and thermal vacuum tests. Figure 9\n\n6\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nshows the EQM being prepared for the vibration tests (left panel) and for the thermal vacuum\ntests (right panel) at SERMS srl in Italy. The EQM is a full size tracker model that has the same\nthermal, mechanical and data interface to the satellite as the final Flight Model (FM). However\nonly 26 functioning silicon ladders have been assembled on the EQM, the remaining 166 being\nmechanical ladders with the same mechanical and thermal properties as the real silicon ladders.\n\nThe EQM has also been integrated into the DAMPE prototype full detector and tested\nextensively with particle beams at CERN PS and SPS during 2014 and 2015. Data with protons,\nelectrons, muons, photons and nuclear fragments have been taken and are being analysed to\nassess the detector performance. Figure 10 shows the DAMPE prototype detector in the CERN\nPS T9 beam line during the 2014 test period (left panel), and an event display of a photon\nconverted in the STK. \n\nFigure 10: The DAMPE prototype detector in the PS T9 beam line(left); An event display of a photon converted in\nthe STK  (right).\n\n4. The Status of the STK Flight Model (FM)\n\nThe STK Flight Model, to be used in the DAMPE payload, has been constructed in April\n2015 in Geneva and tested with cosmic rays for several days, before being delivered to IHEP in\nBeijing. Acceptance level of vibration and thermal vacuum tests were conducted in Beijing in\nMay 2015. In June 2015 the STK FM has successfully passed the DAMPE payload integration\ntest and then the DAMPE satellite integration test. After 2 full months of intensive\nmanipulations and tests the STK remains in excellent quality. At the end of the satellite\nintegration test, the percentage of noisy channels (noise >5 ADC counts) remains below 0.3%.\nOnly 18 (<0.03%) channels out of the 74382 need to be masked (see Figure 11). Currently\nintegration and tests at the satellite level is progressing to meet the scheduled launch at the end\nof 2015. \n\n7\n\n\n\nP\no\nS\n(\nI\nC\nR\nC\n2\n0\n1\n5\n)\n1\n1\n9\n2\n\nThe Silicon-Tungsten Tracker of the DAMPE Mission Xin Wu\n\nFigure 11: Measured noise of 73728 channels of the STK FM (left); Signal to noise ratio for a noise run after\nsatellite integration of all channels. Some cosmic ray contamination in the noise run is visible (right).\n\n5. Conclusions\n\nThe Silicon-Tungsten Tracker (STK) of the DAMPE mission is based on robust\ntechnology of single-sided silicon strip detectors with analog readout. It will play crucial roles in\ncharge track reconstruction, gamma-ray detection, cosmic ray charge measurement, and overall\nparticle identification. After 2 years of intensive design, prototyping, testing and production\nefforts, an Engineering and Qualification Model has been completed and qualified and a Flight\nModel (FM) has been produced and passed acceptance test. The quality of the FM is excellent\nand meets the design specification. \n\nAcknowledgement\n\nThe authors wish to express their gratitude to M. Prest and E. Vallazza of the AGILE Silicon\nTracker collaboration for fruitful discussions on tracker design and for kindly allowing us to use\nthe AGILE silicon sensor geometry. The generosity of CERN for providing beam time\nallocation and technical assistance at the PS and SPS beam lines, as well as general logistical\nand technical support, including the bonding of several ladders, is acknowledged. This work is\nsupported by the Chinese Academy of Sciences, the Swiss National Science Foundation and\nINFN, Italy. \n\nReferences\n\n[1] J. Chang, in The 7th international workshop “Dark Side of the Universe  (DSU 2011)” (2011). \n(http://kitpc.itp.ac.cn/dsu2011/slides/DSU2011-J.Chang.ppt)\n\n[2]  Science Magazine 332 (6032): 904, 20 May 2011.\n\n8"},"fulltext":true,"source":"PoS","key":"aa97d6d6a4c7518fc7fed2f1d1a8edc7","url":"https://inspirehep.net/files/aa97d6d6a4c7518fc7fed2f1d1a8edc7"}],"citeable":true,"$schema":"https://inspirehep.net/schemas/records/hep.json","references":[{"reference":{"urls":[{"value":"http://kitpc.itp.ac.cn/dsu2011/slides/DSU2011-J.Chang.ppt"}],"label":"1","publication_info":{"year":2011},"title":{"title":"Dark Side of the Universe (DSU 2011)"},"misc":["in The 7th international workshop"],"authors":[{"full_name":"Chang, J."}]}},{"reference":{"label":"2","publication_info":{"year":2011},"misc":["Science Magazine 332 (6032): 904, 20 May"]}}],"number_of_pages":8,"legacy_version":"20200405184520.0","inspire_categories":[{"term":"Experiment-HEP"}],"legacy_creation_date":"2016-08-27","author_count":1,"urls":[{"description":"PoS server","value":"http://pos.sissa.it/archive/conferences/236/1192/ICRC2015_1192.pdf"}],"first_author":{"full_name":"Wu, Xin","last_name":"Wu","first_name":"Xin","recid":983103},"control_number":1483465,"dois":[{"value":"10.22323/1.236.1192"}],"earliest_date":"2015-08-07","document_type":["conference paper"],"texkeys":["Wu:2015fdz"],"titles":[{"title":"The Silicon-Tungsten Tracker of the DAMPE Mission"}],"facet_author_name":["983103_Xin Wu"],"license":[{"license":"CC-BY-NC-SA","imposing":"SISSA"}],"imprints":[{"date":"2015-08-07","publisher":"SISSA"}],"_oai":{"sets":["Literature"],"id":"oai:inspirehep.net:1483465","updated":"2023-03-09T17:29:27.175902"},"curated":false,"journal_title_variants":["PoS"]}},{"links":{"bibtex":"https://inspirehep.net/api/literature/1436456?format=bibtex","latex-eu":"https://inspirehep.net/api/literature/1436456?format=latex-eu","latex-us":"https://inspirehep.net/api/literature/1436456?format=latex-us","json":"https://inspirehep.net/api/literature/1436456?format=json","json-expanded":"https://inspirehep.net/api/literature/1436456?format=json-expanded","cv":"https://inspirehep.net/api/literature/1436456?format=cv","citations":"https://inspirehep.net/api/literature/?q=refersto%3Arecid%3A1436456"},"created":"2016-03-30T00:00:00+00:00","id":"1436456","updated":"2023-03-09T18:38:11.397939+00:00","metadata":{"citation_count":0,"authors":[{"full_name_unicode_normalized":"starovoitov, p.","full_name":"Starovoitov, P.","curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/authors/1054268"},"last_name":"Starovoitov","ids":[{"schema":"ORCID","value":"0000-0003-1990-0992"},{"schema":"INSPIRE BAI","value":"P.Starovoitov.1"}],"inspire_roles":["editor"],"signature_block":"STARAVATAVp","uuid":"1291b91b-b65a-48b3-917a-17fc93bc0139","first_name":"P.","recid":1054268},{"affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/03h5rdc49"}],"full_name_unicode_normalized":"skorina, f.","full_name":"Skorina, F.","record":{"$ref":"https://inspirehep.net/api/authors/2510474"},"last_name":"Skorina","ids":[{"schema":"INSPIRE BAI","value":"F.Skorina.1"}],"affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/903385"},"value":"Gomel State U."}],"inspire_roles":["editor"],"signature_block":"SCARANf","uuid":"b403dea8-8946-4707-b9eb-6100b70e65e8","first_name":"F.","recid":2510474},{"full_name_unicode_normalized":"sukhoi, p.","full_name":"Sukhoi, P.","record":{"$ref":"https://inspirehep.net/api/authors/2510476"},"last_name":"Sukhoi","ids":[{"schema":"INSPIRE BAI","value":"P.Sukhoi.1"}],"affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/904724"},"value":"Gomel State Tech. 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G. Dahmke, Mikko Meyer, Laura Vanhoefer\n\nVerlag Deutsches Elektronen-Synchrotron\n\ni\n\n\n\nImpressum\n\nProceedings of the Magellan Workshop 2016\nMarch 17-18, 2016, Hamburg, Germany\n\nConference homepage\nhttps://indico.desy.de/event/13130\n\nSlides at\nhttps://indico.desy.de/event/13130\n\nOnline proceedings at\nhttp://www-library.desy.de/preparch/desy/proc/proc16-05.pdf\n\nThe copyright is governed by the Creative Commons agreement, which allows for free use and distribu-\ntion of the articles for non-commercial activity, as long as the title, the authors’ names and the place of\nthe original are referenced.\n\nPhoto credit:\nESO (back cover, p. 173)\nNASA (front cover)\nPhotographic Archive of the Hamburg Observatory (p. 191)\nMatthias Quad, Geographisch Handtbuch, Cologne, 1600\nmodified, taken from http://libweb5.princeton.edu/visual materials/maps/websites/pacific/\nmagellan-strait/magellan-strait-maps.html (p. v, p. 1)\n\nEditors:\nStefan K. G. Dahmke, Mikko Meyer, Laura Vanhoefer\nSeptember 2016\nDESY-PROC-2016-05\nISBN 978-3-945931-08-0\nISSN 1435-8077\n\nPublished by\nVerlag Deutsches Elektronen-Synchrotron\nNotkestraße 85\n22607 Hamburg\nGermany\n\nPrinted by\nKopierzentrale Deutsches Elektronen-Synchrotron\n\nii\n\n\n\nOrganizing Committee\n\nBenjamin Büttner\nInstitut für Experimentalphysik\nUniversität Hamburg\nLuruper Chaussee 149\n22761 Hamburg\nbenjamin.buettner@desy.de\nScientific and local organizing committee\n\nStefan Dahmke\nHamburger Sternwarte\nUniversität Hamburg\nGojenbergsweg 112\n21029 Hamburg\nsdahmke@hs.uni-hamburg.de\nScientific and local organizing committee\n\nMikko Meyer\nInstitut für Experimentalphysik\nUniversität Hamburg\nLuruper Chaussee 149\n22761 Hamburg\nmikko.meyer@desy.de\nScientific and local organizing committee\n\nLaura Vanhoefer\nMax-Planck-Institut für Physik\n(Werner-Heisenberg-Institut)\nFöhringer Ring 6\n80805 München\nlvanhoef@mpp.mpg.de\nScientific organizing committee and workshop support team\n\nContact: magellan-workshop@desy.de\n\nWorkshop Support Team\n\nHennig Rebber (Universität Hamburg)\nDavid Meyhöfer (Universität Hamburg)\nAnnika Vanhoefer (Universität Hamburg)\nLaura Vanhoefer (Max-Planck-Institut für Physik)\n\niii\n\n\n\nAcknowledgments\nWe would like to thank Sylke Strien from the administration of the Hamburg University and our local\nsupport team for their wonderful help. Furthemore we would like to thank Martin Tluczykont for his\nadvice during the preperation of the workshop. We also thank DESY for enabling us to hold the work-\nshop at the auditorium and the DESY staff for their support.\nFinally, we would also like to thank all speakers and participants for their interest and contributions.\n\niv\n\n\n\nPreface\n\nThe first Magellan Workshop took place on March 17th/18th 2016 in Hamburg. Several topics have been\naddressed during the workshop with the most time devoted to discussing stellar physics and supernova\nphysics. These two key topics are especially interesting to two distinct fields, each with their own\napproach, method and extensive knowledge gained: astrophysics and neutrino physics. It is thus no\nsurprise that the workshop specifically intended to bring these groups together was met with a certain\nenthusiasm. 46 scientists working in institutions across seven countries attended a total of 30 plenary\ntalks during their two days in Hamburg. The workshop then concluded with a trip to the Hamburg\nObservatory, for a tour of the historic and current telescopes.\nWhile supernovae of course were a primary topic, the presentations included many different, exciting\nprojects. From relatively nearby objects in studies on geoneutrinos originating from inside the Earth via\nstellar physics in the refinement of solar models to high energy sources across multiple galaxies to the\nCosmic Microwave Background.\nOne particular subject in the field of supernova physics for the past 29 years of course has been the\nsupernova SN1987A, mostly owed to its fairly recent explosion enabling modern science to observe\nthe event from Earth and space alike both in electromagnetic waves and particle fluxes. These mea-\nsurements have greatly expanded our knowledge as many theories on both the internal mechanisms as\nwell as the evolution of supernovae could consequently be tested. Not only does the Large Magellanic\nCloud which hosts this famous supernova serve as a pictoral symbol for our workshop, it is also now\ncommonly named after Ferdinand Magellan, who was not the first to discover this prominent feature\nin the southern night sky, but the one whose journey brought news of its marvellous sight to European\nastronomers back in the late 16th century. Furthermore this journey shall serve as a symbol of our\nown venture as Magellan led the expedition to circumnavigate the globe entirely, thus advancing the\nknowledge of his contemporaries with every mile travelled, but in doing so also became a connection\nbetween the Atlantic and Pacific oceans. Finding common ground for scientists of different fields to\nmeet on thankfully is a much easier task than navigating the treacherous waters of the Magellan Strait,\nbut nonetheless the Magellan Workshop hopefully opened a strait of sorts for future cooperations.\n\nBenjamin Büttner\nStefan K. G. Dahmke\nMikko Meyer\nLaura Vanhoefer\n\nv\n\n\n\nFerdinand Magellan\n\n1480 Born at Saborosa in Villa Real, Province of Traz os Montes in Portugal\n\n1505 Magellan joins the expedition of Francisco d’Almeida to India\n\n1512 During a posting to Morocco Magellan is badly wounded\n\n1517 Magellan leaves Portugal and heads for Spain\n\n1518 March: Magellan is promoted to the rank of Commander of the Order of Santiago and gets per-\nmission to search for the Spice Islands\n\n1519 10 August: Magellan leaves Seville with five ships\n\n1519 20 September: Magellan leaves Spain to start his voyage\n\n1519 6 December: Crew members spot South America\n\n1520 Easter: While anchored off Patagonia waiting for favourable weather, a mutiny involving three\nout of the five ship captains breaks out\n\n1520 October: After a series of delays, Magellan and his crew resume their voyage on the remaining\nships\n\n1520 21 November: Enters the straits which would be named the Magellan Straits becoming the first\nEuropeans ever to sail across the Pacific Ocean\n\n1521 March 6: Magellan reaches the Pacific island of Guam\n\n1521 16 March: Discovers the Philippines\n\n1521 April 27: Ferdinand Magellan is killed by natives on the island of Mactan. Only 110 of the original\ncrew members remained so they abandoned one of the ships (Conception). On November 6th the\nremaining ships reached the Maluku Islands which was their original destination. Trinidad tried\nto return back to Spain the same way they had came but was forced to return to the Spice Islands\nwhere they were imprisoned by the Portuguese. The Victoria was the last remaining ship.\n\n1522 September 6: The Victoria reaches Sanlucar de Barrameda in Spain with only 18 survivors led by\nthe Spaniard Juan Sebastián Elcano who took over command after Magellan’s death\n\nvi\n\n\n\nContents\n\nPhysics Opportunities with Supernova Neutrinos 3\nNinetta Saviano\n\nNeutrino-Nucleon Interactions in Core Collapse Supernovae 11\nAndreas Lohs, Gabriel Martinez-Pinedo, Tobias Fischer\n\nSearches for point-like neutrino sources with the ANTARES neutrino telescope 19\nJavier Barrios-Martı́\n\nSolar models, neutrinos and composition 27\nAldo Serenelli, Nuria Vinyoles\n\nCNO neutrinos and metallicity of stars 35\nStefano Davini\n\nThe GERDA Experiment: Search for the Neutrinoless Double Beta Decay 41\nMichael Miloradovic\n\nThe atmospheric muon charge ratio: a probe to constrain the atmospheric νµ/ν̄µ ratio 47\nNicoletta Mauri\n\nA Search for Lorentz and CPT Violation Using the T2K Near Detectors 55\nGary Alexander Clifton\n\nSearching for neutrino oscillation parameters in long baseline experiments 65\nSampsa Vihonen\n\nPlanck 2015 Cosmological Results 71\nDaniela Paoletti\n\nThe SNO+ experiment: current status and future prospects. 87\nValentina Lozza\n\nMeasurement of solar neutrino fluxes with Borexino 95\nSimone Marcocci et al.\n\nMonte Carlo simulations in neutrino physics: the example of the SOX experiment 105\nAlessio Caminata et al.\n\nMagellan Workshop 2016 vii\n\n\n\nSolar neutrino detection in a large volume double-phase liquid argon experiment 113\nDavide Franco\n\nNeutrino Astrophysics with JUNO 121\nSebastian Lorenz\n\nLatest results from LUNA experiment 129\nFrancesca Cavanna\n\nConnecting neutrino Astrophysics to Multi-TeV to PeV gamma-ray astronomy with TAIGA 135\nMartin Tluczykont et al.\n\nSimulation of imaging air shower Cherenkov telescopes as part of the TAIGA Project 143\nMaike Kunnas et al.\n\nRecent Results from the MAGIC Telescopes 151\nDorit Eisenacher Glawion\n\nNeutrino Geoscience 157\nLivia Ludhova\n\nSN 1987A - Exciting Physics 165\nBruno Leibundgut\n\nThe SOX experiment: understanding the detector behavior using calibration sources 175\nAlessio Caminata et al.\n\nNeutron Shielding Simulations and Muon-induced Neutrons 181\nLaura Vanhoefer et al.\n\nOn the history of the Hamburg Observatory 193\nStefan K. G. Dahmke\n\nviii\n\n\n\nConnecting Neutrino Physics\nand Astronomy\n\nPlenary Talks\n\nMagellan Workshop 2016 1\n\n\n\n\n\nPhysics Opportunities with Supernova Neutrinos\n\nNinetta Saviano1\n\n1PRISMA Cluster of Excellence and Mainz Institute for Theoretical Physics, Johannes Gutenberg-\nUniversität Mainz, 55099 Mainz, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/16\n\nCore-collapse supernovae (SN) represent a unique laboratory to probe neutrino properties\nin the extreme conditions offered by a stellar gravitational collapse. The role of astrophys-\nical messengers played by neutrinos during a stellar collapse is largely associated with the\nsignatures imprinted on the observable SN neutrino burst by flavor conversions occurring\ndeep inside the star. At this regard, the dense SN core represents a crucial environment to\ninvestigate neutrino flavor mixing in high-density conditions. Indeed, within a radius of a\nfew hundred kilometers, the neutrino gas is so dense to become a “background to itself”,\nmaking the neutrino flavor evolution highly non-linear and leading in some situations to\nsurprising and counterintuitive collective phenomena.\n\n1 Introduction\n\nCore-collapse supernovae (SNe) is one of the most energetic and spectacular phenomena in\nthe Universe. They are powerful explosions which mark the violent death of massive stars\n(M & 8 M⊙) as they become unstable during the late phases of their evolution. The explosion\nis driven by a shock wave that eventually ejects the outer mantle of the collapsing star, liberating\na gravitational binding energy EB ≃ 3 × 1053 erg. Remarkably, 99% of this binding energy is\ncarried away by neutrinos and antineutrinos of all the flavors, making a core-collapse SN one\nof the most powerful neutrino source. Therefore, neutrinos emitted during such a process\nare a promising tool to probe neutrino mass and mixing properties and to provide valuable\ninformation about SN mechanism itself. The neutrino fluxes and spectra are sensitive to many\ndetails of SN physics, notably the progenitor mass, the nuclear equation of state, and the\noccurrence of convection inside and outside the SN core. Originating deep inside the core and\npassing through the mantle of the star, the neutrino flavor conversions are affected by dense\nmatter on their road through the stellar envelope. Moreover, in the deepest SN regions the\nneutrino density is so high that can dominate the flavor evolution, eventually producing a\nsurprising collective behavior in the SN neutrino ensemble. As a consequence of these effects,\nthe SN neutrino fluxes reaching the detectors would be deeply modified with respect to the\ninitial ones, carrying fascinating signatures of oscillation effects occurring in the deepest SN\nregions. Thus detection of a high-statistics neutrino signal from the next nearby SN represents\na new frontier in low-energy neutrino physics and astrophysics. For a more detailed treatment\nsee the recent review [1].\n\nMagellan Workshop 2016 1Magellan Workshop 2016 3\n\n\n\nFigure 1: Neutrino signal of a core-collapse SN for a 10.8M⊙ progenitor [2]. Left: Prompt\nneutrino burst.Middle: Accretion phase.Right: Cooling phase.\n\n2 Neutrino emission\n\nDuring and after the explosion, the star emits neutrinos in three main phases (Fig. 1).\n\n1. Prompt νe burst : as the shock wave passes through the neutrino sphere, a νe burst, lasting\nt ≃ 10 ms, is released due to rapid electron capture on dissociated nuclei (deleptonization\nof the outer core layers) e− + p → n+ νe, leading to a sudden rise in the luminosity up to\nL = 1053 erg s−1. The fluxes of the other neutrino flavors are negligible compared to νe.\n\n2. Accretion phase: the shock wave quickly loses its energy in dissociating nuclei and stag-\nnates at a radius of 150-200 km. Material continues to fall onto the core (accretion shock)\nfor the following few 100 ms and powers the emission of neutrinos and antineutrinos of all\nspecies, with a luminosity of L = 1052 erg s−1 and caracterized by a strong excess of the νe\nspecies over the others. Pronounced hierarchy of average energies 〈Eνx〉 > 〈Eν̄e 〉 > 〈Eνe〉,\nwhere x stands for any of νµ,τ or ν̄µ,τ .\n\n3. Kelvin-Helmholtz cooling phase: after the successful explosion, the remaining proto-\nneutron star cools by neutrino emission over about 10 s. The luminosity is approximately\nequipartitioned between different species L = 3× 1051 erg s−1 and the flavor hierarchy of\naverage energies is probably mild.\n\nA supernova can be roughly considered as a black-body that cools via neutrino emission.\nIndeed, supernova simulations typically provide neutrinos emitted with “quasi-thermal” spec-\ntra. The spectral characteristics evolve in time, are expected to vary for different supernova\nprogenitors and depend on uncertain input physics such as the nuclear equation of state and\nthe treatment of neutrino transport. Typically, one expects 〈Eνe〉 ≃ 10–12 MeV, 〈Eνe\n\n〉 ≃ 12–\n15 MeV, and 〈Eνx〉 ≃ 15–18 MeV.\n\n2 Magellan Workshop 2016\n\nNINETTA SAVIANO\n\n4 Magellan Workshop 2016\n\n\n\nFigure 2: Snapshots of SN potentials for different post-bounce times. Continuous curves refer\nto the matter potential λ, dot-dashed to the neutrino potential µ (quoted in km−1). Figure\nfrom [1].\n\n3 Supernova potential profiles\n\nEffects of flavor conversions on supernova neutrinos depend on the different densities encoun-\ntered by neutrinos in their propagation in the stellar envelope. In Fig. 2 is represented a snap-\nshot of the different interaction potentials associated with different supernova densities obtained\nfrom the Basel-Darmstadt simulations of a 10.8 M⊙ SN progenitor for different post-bounce\ntimes [2]. Self-induced neutrino oscillations, associated with the neutrino-neutrino interactions\nare related to the neutrino number densities through the neutrino-neutrino potential (Fig. 2)\n\nµ =\n√\n2GF [nνe − nνe ] =\n\n1\n\n4πr2\n\n(\nLνe\n\n〈Eνe〉\n− Lνe\n\n〈Eνe〉\n\n)\n, (1)\n\nwhich decreases in time as the SN cools.\nMatter effects on SN neutrinos depend on the potential\n\nλ =\n√\n2GFne , (2)\n\nwhere ne is the net electron density ne encountered by neutrinos in their propagation. The SN\nelectron density profile is non-monotonic and time-dependent and presents an abrupt disconti-\nnuity, corresponding to the position of the shock-front. The two horizontal strips represent the\nregions associated with MSW resonant flavor conversions with the atmospheric oscillation fre-\nquency ωH (violet band) and with the solar one ωL (light blue) for an energy range E ∈ [1−50]\nMeV.\n\nFrom the comparison of the different densities we realize that in the deepest SN regions\n(r < 103 km) nν ≫ ne, except during the accretion phase (t < 0.5 s). When the neutrino density\ndominates over the matter one (as during the cooling phase) self-induced flavor conversions\nwould develop without any hindrance.\n\nConversely, at larger radii, the flavor conversions will be dominated by the ordinary matter\neffects.\n\nMagellan Workshop 2016 3\n\nPHYSICS OPPORTUNITIES WITH SUPERNOVA NEUTRINOS\n\nMagellan Workshop 2016 5\n\n\n\n4 SN neutrino flavor conversions\n\n4.1 Three-neutrino oscillation framework\n\nThe evidence for flavor evolution comes from a series of experiments performed in about four\ndecades of research with very different neutrino beams and detection techniques. Except for few\ncontroversial results from short-baseline neutrino experiments, that point towards the existence\nof extra sterile neutrino states (with m ∼O(1) eV), all the data from the experiments are con-\nsistent with the simplest extension of the standard electroweak model needed to accommodate\nnon-zero neutrino masses and mixings. Namely, three flavor eigenstates νe,µ,τ are mixed with\nthe three mass eigenstates ν1,2,3 through a unitary matrix U , which in terms of one-particle\n\nneutrino states |ν〉, is defined as |να〉 =\n∑3\n\ni=1 U\n∗\nαi|νi〉 .\n\nA common parametrization for the matrix U is [3]:\n\nU =\n\n\n\n\n1 0 0\n0 c23 s23\n0 −s23 c23\n\n\n\n\n\n\n\nc13 0 s13e\n−iδ\n\n0 1 0\n−s13e\n\niδ 0 c13\n\n\n\n\n\n\n\nc12 s12 0\n−s12 c12 0\n0 0 1\n\n\n (3)\n\nwith cij = cos θij and sij = sin θij , θij being the mixing angles and δ ∈ [0, 2π] being the\nCP-violating phase 1.\n\nThe three-neutrino mass spectrum {mi}i=1,2,3 is caracterized by a “doublet” of relatively\nclose states and by a third “lone” neutrino state, which may be either heavier than the doublet\n(“normal hierarchy,” NH) or lighter (“inverted hierarchy,” IH). Typically, the lightest (heaviest)\nneutrino in the doublet is called ν1 (ν2) and the corresponding mass squared difference is defined\nas\n\nδm2 = m2\n2 −m2\n\n1 > 0 (4)\n\nby convention. The δm2 is traditionally named the solar mass squared difference. The lone\nstate is then labeled as ν3, and the physical sign of m2\n\n3 − m2\n1,2 distinguishes NH from IH.\n\nThe second independent squared mass difference ∆m2, called also atmospheric mass squared\ndifference, is\n\n∆m2 =\n\n∣∣∣∣m2\n3 −\n\nm2\n1 +m2\n\n2\n\n2\n\n∣∣∣∣ , (5)\n\nso that the two hierarchies (NH and IH) are simply related by the transformation +∆m2 →\n−∆m2. Numerically, it results that δm2 ≪ ∆m2.\n\nThe latest solar, reactor and long-baseline neutrino oscillation analyses indicate the following\n±2σ ranges for each parameter (95% C.L.) taken from [4]\n\n∆m2 = (2.43+0.12\n−0.13)× 10−3 eV2 , δm2 = (7.54+0.46\n\n−0.39)× 10−5 eV2 ,\n\nsin2 θ12 = (3.08+0.17\n−0.34)× 10−1, sin2 θ23 = (4.37+1.15\n\n−0.44)× 10−1 , sin2 θ13 = (2.34+0.40\n−0.39)× 10−2.\n\n(6)\n\nWithin the three-neutrino scenario, the most important unsolved problems require probing\nthe CP-violating phase δ, the mass hierarchy, and the absolute neutrino masses. In this context,\na high–statistics detection of Galactic SN neutrinos could give a unique help to solve some of\nthese open issues.\n\n1We neglect here extra-phases possible if neutrinos are Majorana particles, since they are not relevant in\noscillations.\n\n4 Magellan Workshop 2016\n\nNINETTA SAVIANO\n\n6 Magellan Workshop 2016\n\n\n\n4.2 Self-induced flavor evolution of SN neutrinos\n\nNeutrinos free streaming beyond the neutrinosphere also interact among themselves (neutrino\nself-interactions). As pointed out in seminal papers [5, 6], in the deepest regions of a SN\nthe neutrino gas is so dense that neutrinos themselves form a background medium triggering\nlarge self-induced flavor conversions in the deepest SN regions [7]. Under these circumstances,\nneutrinos emitted with different energies would be locked to oscillate in a collective fashion.\n\nThe neutrino-neutrino interactions make an additional contribution to the refractive energy\nshift in the neutrino propagation. This term is proportional to the neutrino density making\nthe equations of motion non linear and so numerically challenging. The main complication in\nthe ν-ν refractive term for SN neutrinos is the angular term coming from the current-current\nnature of the weak interactions. Consequently, for the non-isotropic neutrino emission from\nthe SN core this velocity-dependent term would produce a net current that leads to a different\nrefractive index for neutrinos that propagate on different trajectories. This is at the origin of\nthe so-called “multi-angle effects” [7].\n\nThe multi-angle flavor evolution described by non-linear partial differential equations, has\nnot been solved in its full complexity until now.\n\nA few years ago multi-angle simulations were developed within the so-called “bulb model”\nwhere it is assumed that neutrinos are emitted uniformly and half-isotropically (i.e., with all the\noutward-going modes occupied, and all the backward going modes empty) from the surface of\na spherical neutrinosphere, like in a blackbody. Moreover, it is assumed that there is azimuthal\nsymmetry of the neutrino emission at the neutrinosphere and that all the physical conditions in\nthe star only depend on the distance from the center of the star. Despite the numerous studies\non the subject, at the moment a complete picture of the self-induced flavor conversions in SNe\nis still missing. Indeed, it has been recently questioned if removing some of the symmetries as-\nsumed in this model, this would trigger new instabilities in the flavor evolution. By means of a\nstability analysis of the linearized neutrino EoMs [8], it has been pointed out that removing the\nassumption of axial symmetry in the ν propagation, a new multi-azimuthal-angle (MAA) insta-\nbility could emerge in the flavor evolution of the dense SN neutrino gas. The presence of MAA\neffects unavoidably implies the breaking of the spherical symmetry in the flavor evolution after\nthe onset of the flavor conversions. This would lead to a challenging multi-dimensional problem\ninvolving partial differential equations. The MAA instability has shown as self-interacting neu-\ntrinos can spontaneously break the symmetries of the initial conditions. Such insight stimulated\nfurther investigations about the validity of the solution of the SN neutrino EoMs worked out\nwithin the bulb model. It has been recently realized that instabilities may grow once initial\nsymmetries are relaxed, since self-interacting neutrinos can spontaneously break the translation\nsymmetries in time [9, 10, 11] and space.\n\n4.3 Suppression of collective oscillations\n\nPredictions are more robust in the phases where collective effects are suppressed, i.e.:\n\n• Neutronization burst (t< 20 ms), where the νe flux is strongly enhanced with respect to\nνx, while the ν̄e flux is strongly suppressed.\n\n• Accretion phase, where ne− − ne+ ≫ nν̄e − nν̄x . The multi-angle matter effects could\nproduce a large spread in the oscillation frequencies for neutrinos travelling on different\ntrajectories, blocking the self-induced flavor conversions. Dedicated studies have been\n\nMagellan Workshop 2016 5\n\nPHYSICS OPPORTUNITIES WITH SUPERNOVA NEUTRINOS\n\nMagellan Workshop 2016 7\n\n\n\nperformed in [12, 13, 14, 15, 16, 17] using inputs from recent hydrodynamic SN simula-\ntions. It has been found that the large matter term inhibits the development of collective\noscillations at early times for iron-core SNe. The matter suppression of self-induced ef-\nfects for iron-core SNe makes relatively easy the prediction of the oscillation effects during\nthe accretion phase, since the neutrino fluxes will be processed by the only MSW mat-\nter effects. As a consequence, the detection of the SN neutrino signal at early times is\nparticularly relevant for the mass hierarchy discrimination.\n\n5 Observable signatures of SN neutrino flavor conversions\n\nWe present some of the possible observable signatures of flavor conversions in SNe and their\nsensitivity to the neutrino mass hierarchy. For definitiveness, we will refer to a Galactic SN at\na distance d = 10 kpc from the Earth.\n\n(a) Self-induced spectral splits. Observationally, the most important consequence of\nthe self-induced flavor conversions is a swap of the νe and ν̄e spectrum with the non-electron\nspecies νx and ν̄x in certain energy intervals, and the resultant spectral splits at the edges\nof these swap intervals. Some of the spectral splits could occur sufficiently close to the peak\nenergies to produce significant distortions in the SN neutrino signal, observable in the large\nunderground detectors.\n\n(b) Neutrinos from the SN neutronization burst. The νe neutronization burst\nis a particularly interesting probe of flavor conversions, since it can be considered almost as a\n“standard candle” being independent of the mass progenitor and nuclear EoS. Since self-induced\neffects are not operative on the SN νe neutronization burst, the νe flux would be only affected\nby MSW effects. At the very early post-bounce times relevant for the prompt burst, MSW\nflavor conversions would occur along the static progenitor matter density profile. We expect\nthat the observable νe flux at Earth would be\n\nFνe = F 0\nνx (NH) , (7)\n\nFνe = sin2 θ12F\n0\nνe + cos2 θ12F\n\n0\nνx (IH) . (8)\n\nThe observation of the neutronization peak would indicate the inverted mass hierarchy, while\nwe do not expect its detection in NH. Concerning the possibility to detect the νe neutronization\nburst, while Mton class WC detectors measure predominantly the ν̄e flux using inverse beta\ndecay, they are also sensitive to the subdominant νe channel via elastic scatterings on e− (see\nFig.). On the other hand, a large LAr TPC will make the cleanest identification of the prompt\nνe burst through its unique feature of measuring νe charged-current interactions, enabling to\nprobe oscillation physics during the early stage of the SN explosion.\n\n(c) Earth matter effect to discriminate the mass hierarchy. For the SN neutrinos\nreaching the detector from “below”, the Earth crossing would induce an energy-dependent\nmodulation in the neutrino survival probability. The appearance of the Earth effect depends on\nthe neutrino fluxes and on the mixing scenario. In particular, the Earth matter effect should be\npresent for antineutrinos in NH and for neutrinos in IH, providing a potential tool to distinguish\nbetween these two cases. The accretion phase is particularly promising to detect Earth crossing\nsignatures because the absolute SN ν flux is large and the flavor-dependent flux differences are\nalso large.\n\n(d) Rise time of the neutrino signal. The rise time of a Galactic iron-core SN νe\n\nlight curve, observable in large underground detectors, can provide a diagnostic tool for the\n\n6 Magellan Workshop 2016\n\nNINETTA SAVIANO\n\n8 Magellan Workshop 2016\n\n\n\n-10 0 10 20 30 40\nt\npb\n\n(ms)\n\n0\n\n10\n\n20\n\n30\n\n40\n\n50\n\n60\n\n70\n\n80\n\nE\nv\nen\n\nts\n p\n\ner\n b\n\nin\n\nNH\nIH\n\nLS220s11.2c\n\nLS220s27\n\nFigure 3: Neutronization events rate per time bin in a 560-kton WC detector for 27 M⊙ and\n11 M⊙ SN progenitors at d = 10 kpc for both NH (continuous curve) and IH (dashed curve).\n\nneutrino mass hierarchy. Due to the combination of matter suppression of collective effects at\nearly post-bounce times and the presence of the ordinary MSW effect in the outer layers of the\nSN, one expects that the observable ν̄e flux at Earth would be given by\n\nFν̄e = cos2 θ12F\n0\nν̄e + sin2 θ12F\n\n0\nν̄x (NH) , (9)\n\nFν̄e = F 0\nν̄x (IH) . (10)\n\nIt is clear that the Fν̄e flux at the Earth would basically reflect the original F 0\nν̄x flux if IH occurs,\n\nor closely match the F 0\nν̄e flux in NH. Figure 4 shows the expected overall signal rate R(t) in the\n\nIceCube detector for a Galactic SN.\n\n0 0.05 0.1 0.15 0.2\nt (s)\n\n2.5\n\n3\n\n3.5\n\n4\n\nC\no\nu\nn\nts\n\n /\n B\n\nin\n [\n\n1\n0\n\n3\n]\n\nFigure 4: Supernova signal in IceCube assuming a distance of 10 kpc, based on a simulation\nfor a 15 M⊙ SN progenitor from the Garching group. Figure from [1].\n\nMagellan Workshop 2016 7\n\nPHYSICS OPPORTUNITIES WITH SUPERNOVA NEUTRINOS\n\nMagellan Workshop 2016 9\n\n\n\n6 Acknowledgments\n\nN.S. have received funding from the European Research Council (ERC) under the European\nUnion’s Horizon 2020 research and innovation programme (grant agreement No. 637506, “ν\nDirections”.\n\nReferences\n\n[1] A. Mirizzi, I. Tamborra, H. T. Janka, N. Saviano, K. Scholberg, R. Bollig, L. Hudepohl\nand S. Chakraborty, Riv. Nuovo Cim. 39 (2016) no.1-2, 1.\n\n[2] T. Fischer, S. C. Whitehouse, A. Mezzacappa, F.-K. Thielemann and M. Liebendorfer,\nAstron. Astrophys. 517 (2010) A80.\n\n[3] G. L. Fogli, E. Lisi, A. Marrone and A. Palazzo, Prog. Part. Nucl. Phys. 57, 742 (2006).\n\n[4] F. Capozzi, G. L. Fogli, E. Lisi, A. Marrone, D. Montanino and A. Palazzo, Phys. Rev. D\n89, 093018 (2014).\n\n[5] J. T. Pantaleone, Phys. Rev. D 46 (1992) 510.\n\n[6] J. T. Pantaleone, Phys. Lett. B 342 (1995) 250.\n\n[7] H. Duan, G. M. Fuller, J. Carlson and Y. Z. Qian, Phys. Rev. Lett. 97, 241101 (2006).\n\n[8] G. G. Raffelt, S. Sarikas and D. de Sousa Seixas, Phys. Rev. Lett. 111, no. 9, 091101\n(2013) [Erratum-ibid. 113, no. 23, 239903 (2014)].\n\n[9] G. Mangano, A. Mirizzi and N. Saviano, Phys. Rev. D 89, no. 7, 073017 (2014).\n\n[10] S. Abbar and H. Duan, Phys. Lett. B 751, 43 (2015).\n\n[11] B. Dasgupta and A. Mirizzi, Phys. Rev. D 92, no. 12, 125030 (2015).\n\n[12] S. Chakraborty, T. Fischer, A. Mirizzi, N. Saviano and R. Tomàs, Phys. Rev. Lett. 107,\n151101 (2011) [arXiv:1104.4031 [hep-ph]].\n\n[13] S. Chakraborty, T. Fischer, A. Mirizzi, N. Saviano and R. Tomàs, Phys. Rev. D 84, 025002\n(2011).\n\n[14] B. Dasgupta, E. P. O’Connor and C. D. Ott, Phys. Rev. D 85, 065008 (2012).\n\n[15] S. Sarikas, G. G. Raffelt, L. Hudepohl and H. T. Janka, Phys. Rev. Lett. 108, 061101\n(2012)\n\n[16] N. Saviano, S. Chakraborty, T. Fischer and A. Mirizzi, Phys. Rev. D 85, 113002 (2012).\n\n[17] S. Chakraborty, A. Mirizzi, N. Saviano and D. d. S. Seixas, Phys. Rev. D 89, no. 9, 093001\n(2014).\n\n8 Magellan Workshop 2016\n\nNINETTA SAVIANO\n\n10 Magellan Workshop 2016\n\n\n\nNeutrino-Nucleon Interactions in Core Collapse\n\nSupernovae\n\nAndreas Lohs1, Gabriel Martinez-Pinedo2,3, Tobias Fischer 4\n\n1Universität Basel, Departement Physik, Klingelbergstraße 82, 4056 Basel, Switzerland\n2Technische Universität Darmstadt, Schlossgartenstraße 2, 64289 Darmstadt, Germany\n3GSI Helmholtzzentrum für Schwerionenforschung, Planckstraße 1, 64291 Darmstadt, Germany\n4Institute for Theoretical Physics, University of Wroclaw, plac Maksa Borna 9, 50-204Wroclaw,\nPoland\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/28\n\nNeutrino interactions in proto-neutron star matter are a major determinant for many key\nproperties of core-collapse supernovae (CCSNe), such as the explosion mechanism or the\nnucleosynthesis of heavy elements. Recent works identify a need for increasingly precise\ninteraction rates. This work presents an alternative way to compute the relativistic Hartree\nresponse for neutrino-nucleon interactions, improving upon previously derived expressions\nin several aspects. The result will then be compared to the so called elastic approximation\nwhich is widely used in supernova simulations, to asses the validity and limitations of the\nlatter.\n\n1 Introduction\n\nThe gravitational collapse of the inner core of a massive star at the end of its lifetime releases\na huge amount of gravitational binding energy of the order of several 1053 erg. Eventually the\ncollapsing core reaches supranuclear densities, where it is stopped by the strong repulsion of\nthe nuclear interaction. At this point the binding energy is mostly converted into heat, leading\nto temperatures of several tens of MeV in the nascent protoneutron star (PNS). Neutrinos are\nemitted in large numbers both due to thermal production as well as the desire of the matter to\nreach weak chemical equilibrium via electron captures on protons. However for densities above\n1011 g cm−3 the neutrino mean free path (MFP) becomes so short that neutrinos are trapped in\nthe matter, forcing them into local thermal and chemical equilibrium. Nevertheless, due to the\nweak coupling of neutrinos to the matter compared to all other interactions in the supernova\ncore, the diffusion and emission of neutrinos is by far the most efficient way to transport energy\ninto the envelope of the massive star and beyond. Consequently, it was expected early on that\nthe vast majority of the gravitational binding energy will be released in the form of neutrinos,\na prediction that was confirmed by the observation of the neutrino signal from SN1987A.\n\nDuring the first ten seconds after core bounce, the most important region for the formation\nof the neutrino spectra is the so called neutrinosphere, where neutrinos decouple from the\nmatter. The first neutrino signal is an initial deleptonization burst that is emitted after the\nsupernova shock crosses this neutrinosphere. As the shock heats the matter, the heavy nuclei\nof the progenitor star are dissolved into free nucleons. Chemical equilibrium at these high\n\nMagellan Workshop 2016 1Magellan Workshop 2016 11\n\n\n\ntemperatures and densities favours then a significantly more neutron rich composition, causing\nrapid electron captures on free protons and subsequent emission of electron neutrinos on a\ntimescale of only several milliseconds. After that, the neutrino spectra can be approximately\nunderstood as thermal emission from the neutrinosphere.\n\nOnce the neutrinos are outside of the neutrinosphere the probability of scattering or absorp-\ntion in the envelope is rather small. However, since the neutrino luminosity is so large, on the\norder of a few 1052erg s−1, the small fraction of neutrinos that do interact can have an enormous\nimpact on the envelope above the PNS. The most important effect is probably the revival of\nthe shock that leads to the eventual supernova explosion via the so called delayed neutrino\nheating mechanism (for a review on CCSNe simulations and possible explosion mechanisms see\ne.g. [1]). After the shock looses most of its energy by photodissociating heavy nuclei in the\naccretion flow, it quickly stalls. In the standard core-collapse scenario it is then expected that\nthe region below the shock (outside the neutrinosphere) is heated faster by absorbing neutri-\nnos from the PNS than it can cool by emitting neutrinos itself. This process transfers energy\nand momentum from the PNS to the shock, resulting in the eventual explosion. In this context\nmulti-dimensional hydrodynamics have been identified as a most crucial ingredient, allowing for\nmulti-D turbulences and convection which help to increase the heating efficiency. This proved\nnecessary to achieve successful explosion in CCSN simulations. Yet, these advances in hydro-\ndynamics have always been accompanied by essential improvements in the neutrino transport\nscheme and general relativity, as well as the microphysics of nuclear matter, nuclear interaction,\nand weak interaction between neutrinos and matter. Since the predicted explosions still differ\nsignificantly from observed explosion energies, this process of refinement is still ongoing.\n\nNeutrinos also play a very important role in the nucleosynthesis of heavy or rare elements in\nCCSNe. One such scenario is the neutrino driven wind (NDW), matter that is ejected from the\nPNS surface after shock revival. The idea is again that absorptions of neutrinos transfer enough\nenergy and momentum to this matter to drive a matter outflow. Once this wind expands and\ncools, nuclei are formed, with the possibility of reaching very large atomic numbers. Without\ngoing into the details of nucleosynthesis, the outcome of this scenario depends strongly on the\ninitial conditions in the NDW, in particular the expansion timescale, the entropy per baryon and\nthe chemical composition. All of these properties are strongly affected if not fully determined\nboth by the absolute size of the neutrino flux as well as the specific spectral characteristics and\neven spectral differences of the distinct neutrino flavours.\n\nRecent studies strongly hint towards the need to improve the precision of neutrino mi-\ncrophysics. With respect to the neutrino heating mechanism, it was found that reaching a\nsuccessful explosion of a marginal supernova in 3D might depend on 10-20% changes to the\nrate for neutrino-nucleon scattering[2].\n\nFor the neutrino driven wind, it seems that core collapse supernova will not result in condi-\ntions that allow for a so called full r-process [3, 4], building the heaviest elements in the universe.\nHowever, a so called weak r-process is still very much possible [5] and might have contributed\nalready to the early chemical evolution in the universe. Whether the neutrino driven wind will\neventually allow for a weak r-process and to which extent depends on the detailed composition\nof the NDW, which depends in turn on a precise description of neutrino interactions at the level\nof 10%.\n\nEventually, it is predicted that the neutrino signal from a CCSN might undergo significant\nmodifications due to neutrino oscillations [6]. Although this work will not look in detail on\nthe topic of oscillations, referring instead to the various complementing contributions in this\nproceedings, the resulting neutrino spectra are obviously dependent on the initial spectral\n\n2 Magellan Workshop 2016\n\nANDREAS LOHS, GABRIEL MARTINEZ-PINEDO, TOBIAS FISCHER\n\n12 Magellan Workshop 2016\n\n\n\ndifferences between the neutrino flavours.\nAll these findings lead to the conclusion, that current research on core-collapse supernova\n\nand related nuclear astrophysics requires a sufficiently accurate description of neutrino interac-\ntions in hot at dense matter. The purpose of this contribution is to discuss several problems\nand uncertainties at this level of precision and to propose solutions for some of them. Section 2\npresents an alternative formalism for the relativistic Hartree response of neutrino-nucleon inter-\nactions, an expression that yields very precise values for the neutrino MFP at various conditions\nin CCSNe. In Section 3 the relativistic Hartree response is used to assess the limitations of the\nso called elastic approximation[7], an analytic expression for the MFP that is widely used in\nsupernova simulations. Finally, the work is concluded in Section 4.\n\n2 Hartree Response for Neutrino-Nucleon Interaction\n\nThe general derivation of the Hartree response for neutrino-nucleon interactions is the same for\ncharged and neutral-current reactions. This work studies reactions of the type\n\nν1 +N2 → l3 +N4.\n\nThe particle ν1 can represent either a neutrino or an antineutrino, while the final state lepton\nl3 will be a neutrino of the same type (neutral current) or the corresponding charged lepton\n(charged current). For charged leptons the finite mass is taken into account, so that the descrip-\ntion is applicable to charged-current interactions of muon neutrinos. In free space and at the\nenergies encountered in CCSNe, these weak interactions can be described by a current-current\nLagrangian\n\nL =\nG√\n2\nlµj\n\nµ,\n\nwhere G = GFVud for charged current and G = GF for neutral current, with GF the Fermi\ncoupling constant and Vud the up-down element of the CKM-matrix. For reactions with a\nneutrino in the initial state the lepton current reads\n\nlµ = ψ̄3γµ (1− γ5)ψ1,\n\nwhere the ψi are Dirac spinors. Note that for an antineutrino in the initial state, indices 1 and\n3 have to be exchanged. The nucleon current is likewise given by\n\nψ4\n\n[\ncV γ\n\nµ +\niF2\n\n2MN\nσµνqν − cAγ\n\nµγ5\n\n]\nψ2\n\nThe value of the coupling constants cV , cA, and F2 depends on the specific reaction (see e.g.\n[15]) For a noninteracting Fermi gas, the Lagrangian L is equivalent to the matrix element.\nHence, using Fermi’s Golden Rule the mean free path λ, considering final state Pauli blocking,\ncan be computed by\n\n1\n\nλ(E1)\n= 2\n\n∫\nd3ppp2\n\n(2π)\n3\n\n∫\nd3ppp3\n\n(2π)\n3\n\n∫\nd3ppp4\n\n(2π)\n3\n\n〈\n|M|2\n\n〉\n\n16E1E2E3E4\nδ4(P1 +P2 −P3 −P4) f2 (1− f3) (1− f4) ,\n\nMagellan Workshop 2016 3\n\nNEUTRINO-NUCLEON INTERACTIONS IN CORE COLLAPSE SUPERNOVAE\n\nMagellan Workshop 2016 13\n\n\n\nwith the particle four momenta Pi. The fi are standard Fermi distributions.\nWhen studying nucleonic matter at densities above 1012 g cm−3 the assumption of nonin-\n\nteracting nucleons is not justified. Both the strong and the electromagnetic interaction affect\nthe spectra of nucleons and/or the correlations between them. These effects have to be taken\ninto account for neutrino interactions. One approach is to compute the nucleon response via\nthe medium polarization tensor in the so called Hartree approximation [8, 9], which takes into\naccount how the nucleon spectrum is altered by the mean effect of the interaction. Instead,\nthis work computes the Hartree response by replacing the free space nucleon current with an\neffective nucleon current in medium, treating the nucleons as quasi free Fermions. However,\nthe formalism of the medium polarization allows to go beyond the mean field by computing\nthe nucleon response via the random phase approximation (RPA) [8, 10, 11, 12, 13]. The\nRPA includes correlations among the nucleons, stemming from the residual interaction. This\nis particularly relevant for the neutral-current response, where the correlations may be more\nimportant than the mean field, especially for subsaturation densities.\n\nAn advantage of the new formalism is that the relativistic Hartree response is analytically\nmore closed, allowing for a simpler and faster numerical computation. Also, for the first time\na finite lepton mass is taken into account to describe charged current reactions of νµ.\n\nIn astrophysical simulations the nucleonic matter is often described by a relativistic mean\nfield (RMF) equation of state (EOS) [14], in particular by a σωρ-model. Here, the free nucleon\nspectrum is replaced by a quasi-particle dispersion relation, given by\n\nEn,p =\n√\nm∗2\n\nn,p + ppp2 + Un,p,\n\nwhere m∗\nn,p and Un,p are the nucleon effective masses and relativistic mean field potentials,\n\nrespectively, of neutrons and protons. They are directly related to the mean meson fields.\nTo compute the Hartree response, one replaces the free nucleon spinors ψi in the nucleon\n\ncurrent by effective nucleon spinors ψ∗\ni ,\n\nψ∗\ni =\n\n1√\nE∗2\n\ni +m∗2\ni\n\n(\nχs\n\nσσσ·ppp\nE∗\n\ni +m∗\ni\nχs\n\n)\n.\n\nHere χs are normalized Pauli spinors so that χ†\nsχs′ = δss′ , σσσ is the vector of Pauli matrices,\n\nand E∗\ni is given by\n\nE∗\ni = Ei − Ui =\n\n√\nm∗2\n\ni + ppp2i .\n\nNote that ψ∗\ni becomes the free nucleon spinor for vanishing meson fields. Plugging ψ∗ in\n\nthe nucleon current one can derive the squared matrix element in medium\n〈\n|MHRT |2\n\n〉\n. To\n\ncompute then the MFP one only needs to adjust the normalization of the phase space integrals\nto conserve Lorentz invariance,\n\n1\n\nλ(E1)\n= 2\n\n∫\nd3ppp2\n\n(2π)\n3\n\n∫\nd3ppp3\n\n(2π)\n3\n\n∫\nd3ppp4\n\n(2π)\n3\n\n〈\n|MHRT |2\n\n〉\n\n16E1E∗\n2E3E∗\n\n4\n\nδ4(P1 +P2 −P3 −P4) f2 (1− f3) (1− f4) ,\n\nwhere the Fermi distributions for the nucleons describe the quasiparticle distributions,\n\nf2,4(E2,4) =\n[\n1 + exp\n\n([√\nm∗2\n\n2,4 + ppp2 + U2,4 − µ2,4\n\n]\n/T\n\n)]−1\n\n.\n\n4 Magellan Workshop 2016\n\nANDREAS LOHS, GABRIEL MARTINEZ-PINEDO, TOBIAS FISCHER\n\n14 Magellan Workshop 2016\n\n\n\nThe MFP cannot be computed fully analytically. However, it can be closed analytically up to\na 2-dimensional numerical integral,\n\n1\n\nλ\n=\nG2\n\n4π3\n\n1\n\nE2\n1\n\n∫ ∞\n\nE2−\n\ndE2\n\n∫ E3+\n\nm3\n\ndE3f2 [1− f3] [1− f4] Itot.\n\nThe integration limits E2− and E3+ are constraints that arise from four-momentum conserva-\ntion. The term Itot is the fully analytical solution of the relation\n\nItot =\np1p2p3p4\n\n4π2\n\n∫\ndΩ2dΩ3dΩ4dE4\n\n〈\n|MHRT |2\n\n〉\n\n16G2\nδ4(P1 +P2 −P3 −P4)\n\nwith pi = |pppi|. The detailed formalism and derivation will be presented in a future publication.\nPrevious solutions for the relativistic Hartree response were closed only up to three numerical\n\nintegrals [12]. Yet, for neutral-current interactions the above approach can be applied only when\nthe final state neutrino is either distributed isotropically or when final state neutrino blocking\ncan be neglected. Otherwise the factor (1− f3) cannot be factored out of the angular integrals.\nIn that case the relativistic Hartree response as given in the literature cannot be further closed.\nThe following section will therefore focus on charged-current interactions.\n\n3 Comparison to Elastic Approximation\n\nA simplification of the mean free path for neutrino-nucleon interactions that is often used in\nCCSN simulations is the so called elastic approximation [7]. Its advantage is that it delivers\nan analytic expression for λ. Starting from the noninteracting Fermi gas approach, the elastic\n\napproximation is inherently nonrelativistic. Expanding\n〈\n|M|2\n\n〉\nin powers of (E1/m2) and\n\ndropping all terms of order 1 or higher, the squared matrix element simplifies to\n\n〈\n|M|2\n\n〉\n\n16E1E2E3E4\n= c2A (3− x) + c2V (1 + x) ,\n\nwhere x is the angle cosine between initial and final lepton momenta ppp1 and ppp3. Also, one\nassumes that the absolute momentum of the nucleons stays constant, p2 = p4. The final\nstate lepton energy equals then the initial neutrino energy plus the mass difference between\nnucleons, and the mean free path can be derived analytically. It is straightforward to expand\nthis approximation to consider the quasiparticle dispersion relation for nucleons in the mean\nfield theory [3],\n\n1\n\nλelst\n=\nG2\n\nπ\n\n(\nc2V + 3c2A\n\n)\nE3p3 (1− f3)\n\nn2 − n4\n\n1− exp [(η4 − η2) /T ]\nΘ(E3 −m3),\n\nwith\n\nE3 = E1 +m∗\n2 −m∗\n\n4 + U2 − U4 and ηi = m∗\ni + Ui − µi.\n\nAs an improvement over the elastic approximation, analytic correction factors for finite\nnucleon recoil and subleading order terms in the matrix element (often called weak magnetism\n\nMagellan Workshop 2016 5\n\nNEUTRINO-NUCLEON INTERACTIONS IN CORE COLLAPSE SUPERNOVAE\n\nMagellan Workshop 2016 15\n\n\n\nFigure 1: Ratio of mean free path from relativistic Hartree response to elastic approximation\n(bright/red) at low densities and temperatures, compared to nucleon recoil and weak magnetism\ncorrection factor(black). Solid and dashed lines correspond to neutrino and antineutrino ab-\nsorption, respectively.\n\ncorrections) were suggested [15]. Essentially, these correction factors are the ratio of the exact\nfree space cross section and its nonrelativistic approximation. Multiplying with this ratio shall\ncorrect the simplifications that were made in the matrix element and the final lepton phase\nspace factor. This approach is nearly exact for nucleons at low densities and temperatures.\nHowever, it can not correct the approximations that were made in the Pauli blocking factors.\n\nFor charged-current reactions, the appropriate correction factor is\n\nR(E1) =\n\n{\nc2V\n\n(\n1 + 4e+\n\n16\n\n3\ne2\n)\n+ 3c2A\n\n(\n1 +\n\n4\n\n3\ne\n\n)2\n\n± 4 (cV + F2) cAe\n\n(\n1 +\n\n4\n\n3\ne\n\n)\n\n+\n8\n\n3\ncV F2e\n\n2 +\n5\n\n3\ne2\n\n(\n1 +\n\n2\n\n5\ne\n\n)\nF 2\n2\n\n}\n/\n[(\nc2V + 3c2A\n\n)\n(1 + 2e)3\n\n]\n.\n\nLooking at the elastic approximation with nucleon recoil and weak magnetism corrections,\none can compare it to the exact mean free path (bar correlations) from the Hartree response,\nin order to assess the precision of the former for various conditions in a CCSN. The situation\nat lower densities and temperatures, corresponding e.g. to the region in the neutrino driven\nwind, is investigated in Fig.1. Here the ratio between the Hartree response and the elastic\napproximation is shown along the analytic correction factor. Note that in both Fig.1 and\nFig.2 the underlying composition and interaction of the nucleons is described by the Hempel-\nEOS [14] in the DD2-parametrization [16]. However, all findings presented here are practically\nindependent of the chosen RMF-parametrization. For a temperature of T = 0.9MeV and\ndensity ρ = 109 g cm−3 one can see a nearly perfect agreement between the two. This seems\nreasonable, as final state blocking is probably negligible under these conditions and the mean\nfree path is almost proportional to the cross section. Also the thermal energy of the nucleons\nis not very large, justifying the assumption of an initial nucleon at rest. It seems safe to use\nthe corrected elastic approximation e.g. to determine the equilibrium electron fraction in the\n\n6 Magellan Workshop 2016\n\nANDREAS LOHS, GABRIEL MARTINEZ-PINEDO, TOBIAS FISCHER\n\n16 Magellan Workshop 2016\n\n\n\nNDW when the neutrino spectrum from the proto-neutron star is known.\n\nFigure 2: Ratio of mean free path from relativistic Hartree response to elastic approximation\n(bright/red) at low densities and temperatures, compared to nucleon recoil and weak magnetism\ncorrection factor(black). Solid and dashed lines correspond to neutrino and antineutrino ab-\nsorption, respectively.\n\nHigher densities and temperatures are studied in Fig.2. At a temperature of T = 5MeV and\na density of ρ = 1013 g cm−3 one finds that the corrected elastic approximation underestimates\nthe mean free path for almost all energies, both for neutrinos and antineutrinos. The deviation\nis on the order of 10%, and it can reach more than 30% for neutrinos in the energy range\naround 20MeV, which is particularly important for neutrino transport and emission in CCSN.\nTherefore it is likely that this might affect the neutrino spectrum in the early cooling and\nNDW phase, starting several hundred seconds post bounce. During the accretion and explosion\nphase, the electron type neutrinos decouple mostly from lower densities, where this deviation\nis smaller. However, one can find deviations on the order of 10% already above densities\nρ = 1012 g cm−3. Also, for temperatures above 5MeV an increase in the Hartree response\ncompared to the corrected elastic approximation by several percent is observed, even for the\nlower densities, reaching ∼ 10% for T = 10MeV. Such temperatures can be realized at the\nneutrinospheres in the early postbounce phase, potentially affecting the accretion and explosion\nphase.\n\nThe general understanding behind the suppression of the elastic approximation compared to\nthe Hartree response is that the restrictions on momentum and energy exchange force the final\nstate particles into configurations with too large Pauli blocking. The correction factor cannot\nimprove this situation since it does not address the statistical factors. Ultimately, further work\nhas to be done to understand this observation in more detail. Yet one can conclude that the\nelastic approximation, despite adding nucleon recoil and weak magnetism corrections, does not\nreach a precision beyond the 10% level for charged current reactions of electron type neutrinos\nat high densities (relevant only later times) and/or high temperatures (relevant at early times\npostbounce). However, the approximation is well suited for conditions such as in the NDW,\nwhere it reproduces the Hartree response with great accuracy.\n\nMagellan Workshop 2016 7\n\nNEUTRINO-NUCLEON INTERACTIONS IN CORE COLLAPSE SUPERNOVAE\n\nMagellan Workshop 2016 17\n\n\n\n4 Conclusion\n\nThe relativistic Hartree response for weak neutrino-nucleon interactions was derived different\nfrom the standard formalism in the literature. For charged-current reactions, the resulting new\nexpression is analytically more closed and allows to consider finite lepton masses like in charged-\ncurrent νµ-absorption. The improvements can also be applied to neutral-current scatterings, if\nthe neutrino distribution is isotropic or final state lepton blocking is negligible. The Hartree\nresponse was compared to the elastic approximation with nucleon recoil and weak magnetism\ncorrections to assess the precision of the latter in CCSNe simulations. It was found that the\naccuracy will be less than 10% for temperature above 5 to 10MeV and densities higher than\n1012 g cm−3. The likely reason is an artificially strong final state blocking enforced by the rigid\nkinematic constraints of the elastic approximation. Hence, it must be used with caution both\nduring the early accretion and explosion phase as well as the initial cooling phase. Nevertheless\nit is well suited to describe neutrino-nucleon interactions in the neutrino driven wind, which\ntakes place at lower densities and temperatures. Future studies should try to understand the\nlimitations of the elastic approximation better. Also it would be interesting to compare both\nthe Hartree response and the elastic approximation in the framework of a dynamical CCSN\nsimulation to quantify the impact of different rates both on the explosion mechanics and the\nsubsequent neutrino spectrum in the initial cooling phase.\n\nAcknowledgements\n\nThe work of A. Lohs was partly supported by the ERC Advanced Grant “FISH”.\nT.F. acknowledges support from the Polish National Science Center (NCN) under grant\n\nnumber UMO-2013/11/D/ST2/02645.\nG.M.P. was partly supported by the Deutsche Forschungsgemeinschaft through contract\n\nSFB 1245, and the Helmholtz Association through the Nuclear Astrophysics Virtual Institute\n(VH-VI-417).\n\nReferences\n[1] H. T. Janka, Ann. Rev. Nucl. Part. Sci. 62 407 (2012).\n\n[2] T. Melson, H. T. Janka, R. Bollig, F. Hanke, A. Marek and B. Müller, Astrophys. J. 808 no.2, L42 (2015).\n\n[3] G. Martinez-Pinedo, T. Fischer, A. Lohs and L. Huther, Phys. Rev. Lett. 109 251104 (2012).\n\n[4] L. F. Roberts, S. Reddy and G. Shen, Phys. Rev. C 86 065803 (2012)\n\n[5] G. Martinez-Pinedo, T. Fischer and L. Huther, J. Phys. G 41 044008 (2014).\n\n[6] H. Duan, G. M. Fuller, Y. Z. Qian, Ann. Rev. Nucl. Part. Sci. 60 569 (2010).\n\n[7] S. W. Bruenn, Astrophys. J. Suppl. 58 771 (1985).\n\n[8] C. J. Horowitz and K. Wehrberger, Phys. Lett. B 266 236 (1991).\n\n[9] S. Reddy, M. Prakash and J. M. Lattimer, Phys. Rev. D 58 013009 (1998).\n\n[10] A. Burrows and R. F. Sawyer, Phys. Rev. C 58 554 (1998).\n\n[11] A. Burrows and R. F. Sawyer, Phys. Rev. C 59 510 (1999).\n\n[12] S. Reddy, M. Prakash, J. M. Lattimer and J. A. Pons, Phys. Rev. C 59 2888 (1999).\n\n[13] C. J. Horowitz and M. A. Perez-Garcia, Phys. Rev. C 68 025803 (2003).\n\n[14] M. Hempel and J. Schaffner-Bielich, Nucl. Phys. A 837 210 (2010).\n\n[15] C. J. Horowitz, Phys. Rev. D 65 043001 (2002).\n\n[16] S. Typel, G. Ropke, T. Klahn, D. Blaschke and H. H. Wolter, Phys. Rev. C 81 015803 (2010).\n\n8 Magellan Workshop 2016\n\nANDREAS LOHS, GABRIEL MARTINEZ-PINEDO, TOBIAS FISCHER\n\n18 Magellan Workshop 2016\n\n\n\nSearches for point-like neutrino sources with the\n\nANTARES neutrino telescope\n\nJ. Barrios-Mart́ı1 on behalf of the ANTARES collaboration\n\n1Instituto de F́ısica Corpuscular (CSIC-Universitat de València) c/ Catedrático José Beltrán,\n2 E-46980 Paterna, Valencia, Spain\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/26\n\nANTARES is the largest undersea neutrino telescope currently in operation. This tele-\nscope has been taking data continuously since 2007 with the aim to detect neutrinos from\nastrophysical sources. A review of the point-like neutrino sources with the ANTARES\ntelescope is presented. The latest results of the all-flavour neutrino point-source analysis\nwith data from 2007 to 2013 and the results of the first combined ANTARES and IceCube\nanalysis are described.\n\n1 The ANTARES neutrino telescope\n\nANTARES is the first undersea neutrino telescope [1]. It is in continuous operation since 2006,\nand completed since 2008. It is located in the depth of the Mediterranean Sea (2475 m deep)\nat coordinates (42◦ 48’ N, 6◦ 10’ E), 40 km South of Toulon (France). It consists of 12 lines of\n350 m height with inter-line separations between 60 to 75 m, which house a total of 885 Optical\nModules (OMs). An OM consists of a 10” photomultiplier tube (PMT) contained inside a\n17” glass sphere. The OMs are facing downward at an angle of 45◦ in order to improve the\ndetection of up-going muon-neutrinos. They are organized in triplets, so that there are 25\ntriplets (storeys) on each line, with a distance between consecutive storeys in the same line of\n15m.\n\nThe detection principle is based on the indirect detection of high energy neutrinos which\ninteract via charged current (CC) or neutral current (NC) interactions with a nucleus inside\nor nearby the detector volume. In case a muon neutrino interacts via CC, it produces a muon\nwhich can travel a long distance before decaying. Since this muon travels at relativistic speeds\nin water, Cherenkov radiation is emitted. This type of events are usually referred as track-\nlike. A fraction of the photons emitted from this radiation are detected by the PMTs, which\nproduces a signal or hit. With the collected charge and the time of arrival, a reconstruction of\nthese events is performed. Tau neutrinos can also produce this type of events.\n\nOn the other hand, all neutrinos interacting via NC or electron neutrinos and sometimes\ntau neutrinos via CC can produce cascade-like events. In this case, a fraction of the original\nneutrino energy is deposited via a hadronic and/or an electromagnetic shower. The photons\ncoming from these showers expand spherically from the interaction vertex. The reconstruction\nis performed with the detected photons coming from the shower.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 19\n\n\n\n2 ANTARES point-source search including cascades\n\nFor the first time, a search for point-like sources including cascades has been performed. The\naddition of cascades provides the ability to perform these searches with all flavours of neutrinos\nboth via charged current and neutral current interactions.\n\n2.1 cascade reconstruction\n\nlog(EMC/GeV)\n\n2 3 4 5 6 7 8\n\nm\ned\nia\nn\nξ/\n°\n\n0\n\n2\n\n4\n\n6\n\n8\n\n10\n\n12\n\n14 νe CC\nνx NC\n\nPreliminary\n\nlog(EMC-Shower/GeV)\n\n2 3 4 5 6 7 8\n\nm\ned\nia\nn\nE\n\nre\nc\no\n/\nE\n\nM\nC\n-S\nh\no\nw\ne\nr\n\n0\n\n0.2\n\n0.4\n\n0.6\n\n0.8\n\n1\n\n1.2\n\n1.4\nνe CC\n\nνx NC\n\nPreliminary\n\nFigure 1: Left: Angular resolution for cascades coming from νe charged current interactions\n(red) and from NC interactions of neutrinos of all flavours (blue). Right: median energy\nresolution between the reconstructed energy and the deposited energy of the interaction. Both\nfigures have been calculated for events with neutrino-interaction vertices of |Zvertex| < 200 m,\nρvertex < 90 m from the detector centre. The error-bars in the y axis denote the 1σ region.\n\nA new reconstruction for cascade events [2] was developed to include these events in point-\nlike source searches. This reconstruction is based on a two-step procedure.\n\nThe first step consists on the interaction vertex reconstruction, where an expanding spheri-\ncally shell of photons from this point is assumed. This is done via a pre-fit with the information\nof all selected hits, in which we assume that they have been produced from the same interaction.\nThat leads to a set of equations which are minimised via an M-estimator fit,\n\nMEst =\n\nN∑\n\ni\n\n(\nqi\n\n√\n1 + t2i−res/2\n\n)\n(1)\n\nwhere qi and ti−res denote the charge and the time residual (difference between the expected\nand the actual arrival time) of the hit i.\n\nThe second step consists on the direction reconstruction. In this case, all hits coming from a\ntriggered event in a time residual window of -200 < tres < 500 ns are considered, where the tres\nis the time residual with respect to the previously fitted vertex. The direction of the cascade is\nthen obtained by performing a maximisation of a likelihood, defined as:\n\n2 Magellan Workshop 2016\n\nJAVIER BARRIOS-MARTÍ\n\n20 Magellan Workshop 2016\n\n\n\nL =\n\nNselected∑\n\ni=1\n\nlog [Pqi>0(qi|Eν , di, φi, αi) + Pbg(qi)]\n\n+\n\nNPMTswithout hits∑\n\ni\n\nlog [Pq=0(Eν , di, φi)] . (2)\n\nIn this equation, Pqi>0 denotes the probability for a PMT, i, to have measured a charge\nqi, Pq=0 is the probability for an absence of a detection in a PMT, and Pbg is the probability\nfor a hit to be originated via background. All of these probabilities depend on the charge of\nthe hit, qi. Also, Pqi>0 and Pq=0 depend on the neutrino energy, Eν , the distance between the\nPMT i and the vertex of the interaction, di, the angle between the neutrino direction and the\nvector which connects the PMT i and the vertex of the interaction, φi, and the angle between\nthe direction of the PMT with the same vector, αi.\n\nThe performance of this reconstruction can be seen in figure 1. A median angular resolution\nbetween 2◦ and 4◦ degrees can be achieved for contained events (events within |Zvertex| < 200\nm, ρvertex < 90 m from the detector center according to our simulations) with neutrino energies\nof Eν between 103 and 106 GeV for electron neutrino events interacting via CC. An estimate\nof the angular resolution of the cascade reconstruction can be obtained by using a data sample\ndominated by atmospheric muons. These events can be reconstructed both as track-like events\nand as cascades. Figure 2 shows the angular difference between the directions obtained from\nthese two reconstructions for a loose cut dominated by atmospheric muons. The low difference\nbetween the reconstructed directions of both reconstructions shows the performance of the\ncascade reconstruction.\n\n∠(Track, Shower)/°\n0 5 10 15 20 25 30\n\nar\nb\nit\nra\nry\n\nn\no\nrm\n\nal\nis\nat\nio\nn\n\n0\n\n1\n\n2\n\n3\n\n4\n\n5\n\n6\n\n7\n\n8\n\nData\natm. Muons\natm. Muons, small Error\n\nPreliminary\n\nFigure 2: Angular difference between the reconstructed directions given by the track and cascade\nreconstructions. Black dots: Values obtained from data. Blue line: Values obtained from\nsimulations. Magenta line: Values obtained for atmospheric muons from simulations with an\nangular resolution in the cascade channel lower than 5◦.\n\nMagellan Workshop 2016 3\n\nSEARCHES FOR POINT-LIKE NEUTRINO SOURCES WITH THE ANTARES NEUTRINO . . .\n\nMagellan Workshop 2016 21\n\n\n\n2.2 Search method\n\nThe data sample contains data recorded from Jan 29, 2007 to Dec 31, 2013. The event selection\nfor the track-like channel is identical to the last published point-source analysis [3], which\nconsists on a cut on the reconstructed zenith angle, cos(θtrack) < 0.1, an angular error estimate\nof 0 < βtrack < 1◦, and a quality of the reconstructed track of Λtrack > -5.2. These parameters\nare obtained from the track reconstruction mechanism, which uses a maximum likelihood (ML)\nmethod [4, 5]. This reconstruction is based on a multi-step procedure to fit the direction of the\nmuon by maximising the Λtrack parameter. The angular error estimate, σ, is calculated from\nthe uncertainty on the zenith and azimuth angles extracted from the covariance matrix.\n\nThe event selection for the cascade channel is obtained via a set of multiple cuts. First of\nall, a veto of the events which have been selected in the track channel is performed. Secondly,\nonly events reconstructed as up-going or with almost horizontal directions (cos(θsho) < 0.1),\nand with a reconstructed interaction vertex close or inside the detector volume (ρshow < 300m,\nZshow < 250m) are included. Additionally, a maximum angular error estimate of 10◦ degrees\nis allowed. A combined cut on the GridFit Ratio [6] parameter and the number of selected\nhits by the cascade reconstruction is performed. Finally, a cut on a muon/cascade likelihood\ndiscrimination parameter and a cut on the ratio between the collected charge before and after\nthe expected expected time recorded by the OMs is applied.\n\nAfer this criteria, a total number of 6490 track-like and 172 cascade-like neutrino candidates\nare obtained. Figure 3 shows the location of these events in equatorial coordinates.\n\nα = −180°α = 0°α = 180°\n\nδ = −90°\n\nδ = −60°\n\nδ = −30°\n\nδ = 0°\n\nδ = 30°\n\nδ = 60°\n\nTracks\n\nShowers\n\nPreliminary\n\nFigure 3: Skymap in equatorial coordinates of the reconstructed directions of the track-like\n(green) and cascade-like (red) events.\n\nAn unbinned likelihood maximisation with an extended likelihood definition is performed in\norder to search for excesses of events. This likelihood takes into account the directional infor-\nmation and the reconstructed number of hits, N as information of each event. The likelihood,\nas a function of the total number of fitted signal events, µsig, can be expressed as:\n\n4 Magellan Workshop 2016\n\nJAVIER BARRIOS-MARTÍ\n\n22 Magellan Workshop 2016\n\n\n\nlogLs+b =\n∑\n\nS\n\n∑\n\ni∈S\nlog\n\n[\nµS\nsig · PSFS(∆Ψi) ·NS\n\nsig(Ni) +B S(δi) ·NS\nbkg(Ni)\n\n]\n− µS\n\nsig (3)\n\n+ P (µsh\nsig|µtr\n\nsig · Ash(δs)/Atr(δs)).\n\nwhere S denotes the track (tr) or cascade (sh) sample, PSF(∆Ψi) is the point spread function\nvalue for a given event i at a distance ∆Ψi from the assumed source, B(δ) is the background\nrate of data events as a function of the declination δ, NS\n\nsig(N ) and NS\nsig(N ) are the probability\n\ndensity functions which give the probability for an event to be signal or background given\nthe number of hits N , and AS(δs) is the acceptance (proportionality constant between a flux\nnormalisation, Φ0 and the number of expected events detected given an E−2 source spectrum\nat a declination δs). The poissonian term, P (µsh\n\nsig|µtr\nsig · Ash(δs)/Atr(δs)) is included to take into\n\naccount the expected ratio between the number of signal events between the track and cascade\nchannels. Figure 4 shows the acceptance for the cascade and track channels.\n\nsin(δ)\n\n−1 −0.5 0 0.5 1\n\nA\n/\n(1\n0\n8\nG\neV\n\n−\n1\ncm\n\n2\ns)\n\n0\n\n0.5\n\n1\n\n1.5\n\n2\nTracks\n\nShowers× 3\n\nPreliminary\n\nFigure 4: Acceptance for the track (blue) and cascade (red) channel assuming an E−2 spectrum.\nThe cascade acceptance has been multiplied by 3 in this figure.\n\nFrom this likelihood, a test statistic, TS, is defined so that TS = logLs+b − logLb, where\nLs+b is the maximised likelihood, and Lb is the likelihood value for the background only case.\n\nDifferent search strategies are followed in order to look for candidate clusters which could\nexceed the expected background. In the full sky search, the whole sky is scanned. Per each\ncluster, also the direction of the hypothetical source is fitted. In the candidate list search, the\nlocation of 52 known high-energy gamma-ray emitters are considered, where the coordinates of\nthe source are fixed. A separate candidate list search for the location of the eight track-like\nevents from the Southern Sky observed by the IceCube experiment in their 3 year High Energy\nneutrino sample [7] is considered, where the fitted location of the fitted source is set to twice the\nangular error estimate of the IceCube events. Finally, a scan over the region around the galactic\ncenter (half-axis radii of 30◦ in galactic longitude, and 15◦ degrees in latitude) is performed.\n\nMagellan Workshop 2016 5\n\nSEARCHES FOR POINT-LIKE NEUTRINO SOURCES WITH THE ANTARES NEUTRINO . . .\n\nMagellan Workshop 2016 23\n\n\n\n2.3 Results\n\nNo significant clusters of events are observed in this analysis. The most significant cluster in the\nfull sky search is found at a location of (α, δ) = (–46.8◦, –64.9◦) in equatorial coordinates with\na post-trial significance of 4.2% (2.0σ in the two-sided convention). A total of 17(7) track-like\nevents are observed within 3(1)◦ degrees and 1 cascade event within 10◦. The most significant\ncluster of the candidate list search corresponds to the one corresponding to the location of\nHESSJ0632+057, with a post-trial significance of 20% (1.27σ). A total of 6(1) track events are\nobserved within 3(1)◦, and a total of 3 cascades are observed within 15◦. The most signal-like\ncluster corresponding with the location of an IC event corresponds to (α, δ) = (130.7◦, 29.5◦),\nassociated with IC track ID3. Finally, the most significant cluster of the search around the\nGalactic Centre is observed at (α, δ) = (110.0◦, 50.8◦) with a post-trial significance of 74%\n(0.33σ).\n\nLimits from the 52 candidate sources and the sensitivity at a 90% confidence level for a\nfixed-source search with an E−2 energy spectrum are reported in figure 5, which present the\nbest sensitivities for a large fraction of the Southern sky. The comparison with the 4 year\nanalysis of IceCube is also presented.\n\nδ/°\n\n−100 −80 −60 −40 −20 0 20 40\n\nE\n2\nd\nΦ\n/\nd\nE\n/\n(G\n\neV\ncm\n\n−\n2\ns−\n\n1\n)\n\n10−9\n\n10−8\n\n10−7\n\nANTARES limits\n\nANTARES sensitivity\n\nIceCube sensitivity\n\nFigure 5: 90%CL limits and sensitivities following the Neyman approach [8]. The blue dots\ncorrespond to the limits of the 52 candidate sources. The blue line denotes the sensitivity for a\npoint-source using a fixed-source analysis in this analysis. As a comparison, the sensitivity for\nthe 4 year point-source analysis from IceCube is present.\n\n6 Magellan Workshop 2016\n\nJAVIER BARRIOS-MARTÍ\n\n24 Magellan Workshop 2016\n\n\n\n3 Combined ANTARES-IceCube point-source analysis\n\nThe results of a search for point-like sources with data collected from the ANTARES and\nIceCube experiments were published recently[9]. Despite the significantly smaller volume of\nANTARES compared with IceCube, its location provides a view of the Southern sky with\nsignificantly reduced background for neutrino energies below 100 TeV. The complementarity\nof both detectors with respect to sources coming from the Southern sky is due to the different\ngeographical location, size and atmospheric muon background, allowing for a gain in sensitivity\nby combining data from both detectors.\n\nThe data sample corresponds to all events coming from the Southern Sky included in the\nthree-year IceCube point-source analysis [10] and the 2007-2012 ANTARES one [3]. No cascade-\nlike events were included in this analysis. The ANTARES sample contains data recorded from\nJan 29, 2007 to Dec 31, 2012, whereas the IceCube one corresponds to data collected from Apr\n5, 2008 to May 13, 2011 with the partially completed detector, and without the use of the Deep\nCore strings.\n\nNo significant clusters were found in this search. Figure 6 shows the sensitivities and limits\nobtained for the cases of an E−2 and a E−2.5 energy source spectra. They are compared with\nthe individual samples used in this analysis. The region of improvement due to the combination\nof the samples varies with the assumed energy spectrum of the source. An improvement factor\nof about 2 can be reached for an E−2 spectrum in regions close to the Galactic Centre. For\nsteeper energy spectra the area of the sky with an improved sensitivity switches to declinations\ncloser to the equator.\n\nFigure 6: Point source sensitivities and limits for a 90% CL using the Neyman method [8]\nfor energy spectra of E−2 (left) and E−2.5 (right). The green line indicates the sensitivity for\nthe combined search. Red and blue curves indicate the sensitivities for the individual IceCube\nand ANTARES analyses, respectively. As reference, the declination of the Galactic Center is\napproximately at sin(δ = −29◦) ≈ -0.48.\n\nMagellan Workshop 2016 7\n\nSEARCHES FOR POINT-LIKE NEUTRINO SOURCES WITH THE ANTARES NEUTRINO . . .\n\nMagellan Workshop 2016 25\n\n\n\n4 Conclusions\n\nA review of the latest results of point-like neutrino source searches with the ANTARES neutrino\ntelescope has been shown. The first search for point-sources using both cascade and track\nevents has been presented, with no statistically significant signal clusters found. A specific\nreconstruction for cascades had to be developed, leading to median angular resolutions between\n2 and 4◦ for energies between 103 and 106 GeV. 90% CL limits and sensitivities for this search\nhave been presented. A brief summary of the first combined analysis using data both from the\nANTARES and IceCube experiments is also given, where an improvement up to a factor of 2\nin sensitivity is achieved.\n\nAcknowledgments\n\nWe gratefully acknowledge the financial support of Plan Estatal de Investigación (refs. FPA2015-\n65150-C3-1-P, -2-P and -3-P, (MINECO/FEDER)), Severo Ochoa Centre of Excellence and\nMultiDark Consolider (MINECO), Prometeo and Grisoĺıa programs (Generalitat Valenciana),\nSpain., and Atracció de Talent of Universitat de València, Spain.\n\nReferences\n[1] M. Agueron et al. (ANTARES Collaboration), Nucl. Instrum. Meth., A656, 11, (2011).\n\n[2] T. Michael, Light at the end of the Shower, PhD Thesis, NIKHEF, Amsterdam (2016).\n\n[3] S. Adrián-Mart́ınez et al. (ANTARES Collaboration) ApJ, 786, L5 (2014).\n\n[4] S. Adrian-Martinez et al. (ANTARES Collaboration) ApJ, 760, 53 (2012).\n\n[5] S. Adrian-Martinez et al. (ANTARES Collaboration) JCAP, 1303, 006 (2013).\n\n[6] E. L. Visser, Neutrinos from the Milky Way, PhD Thesis, NIKHEF, Amsterdam (2015).\n\n[7] M. G. Aartsen et al. (IceCube Collaboration), Phys. Rev. Lett. 113, 101101 (2014).\n\n[8] Neyman J. Phil. Trans. Royal Soc. London A, 236, 333 (1937).\n\n[9] Adrian-Martinez, S. et al. (ANTARES and IceCube Collaborations), ApJ. 823, 65 (2016).\n\n[10] Aartsen M. G. et al. (IceCube Collaboration), ApJ, 779, 132 (2013).\n\n8 Magellan Workshop 2016\n\nJAVIER BARRIOS-MARTÍ\n\n26 Magellan Workshop 2016\n\n\n\nSolar models, neutrinos and composition\n\nAldo Serenelli1, Nuria Vinyoles1\n\n1Institute of Space Sciences (IEEC-CSIC), Carrer de Can Magrans S/N, 08193, Barcelona,\nSpain\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/32\n\nIn this presentation we review the current status of standard solar models (SSMs) and\nthe solar abundance problem. We show that the most recent generation of SSMs (B16)\nyields neutrino fluxes that favors solar models with high metallicity interiors. This is in\nagreement with helioseismic probes of the solar interior, which also favor high-metallicity\nsolar models. For the first time, both solar neutrino fluxes and helioseismology point\ntowards the same preferred solution. However, because available neutrino data refers\nonly to fluxes coming from the pp-chain, this preference is not towards a certain solar\ncomposition but rather towards a given internal temperature profile. At this point, the\ndegeneracy between solar composition and radiative opacities cannot be broken. Future\nCNO solar neutrino experiments are the most promising way of elucidating the answer\nto this conundrum. In this presentation, we also show that for cases where the detailed\nsolar composition is not a critical factor, SSMs are an excellent framework over which to\ntest non-standard physics in solar (stellar) interiors and a laboratory for particle physics\nregardless of the solar abundance problem.\n\n1 Introduction\n\nAbout 15 years have passed since neutrino flavor oscillations were confirmed in solar neutrinos\ndirectly by the measurements of the 8B solar neutrino flux, Φ(8B), by SNO [1, 2]. The discovery\ncame after more than 30 years of controversy, following the initial results of the Homestake\nexperiment [3], that have seen neutrino experiments, solar models and incomplete understanding\nof the physics of neutrinos being discussed as possible culprits of the solar neutrino problem.\nKamiokande first [4], and the gallium experiments afterwards (SAGE [5, 6] and GALLEX\n[7]), confirmed the problem of the missing neutrinos. At the same time they established a\npattern of fluxes with strong indications that an astrophysical solution, i.e. lowering the fluxes\npredicted by solar models, could not be reconciled with the experiments. Moreover, during\nthe mid 90s, helioseismology was established as a reliable diagnostics tool of the structure of\nthe solar interior, and the agreement between standard solar models (SSMs) and helioseismic\ninferences was astoundingly good [8, 9]. As a result, the second half of that decade saw a\nrapidly decreasing number of advocates favoring a solar model solution to the solar neutrino\nproblem. Then, Super-Kamiokande discovered oscillations of atmospheric neutrinos [10]. And\nsoon, SNO not only discovered solar neutrino oscillations but showed that SSM predictions for\nthe Φ(8B) flux were spot on [11, 12]. These two experiments then firmly established the reality\nof neutrino flavor oscillations, showing at the same time the incompleteness of the standard\nmodel of particles and the soundness of SSMs as an accurate description of many properties of\n\nMagellan Workshop 2016 1Magellan Workshop 2016 27\n\n\n\nthe Sun [11, 13]. A recent historical account of the development of solar neutrino experiments\nand models can be found in [14].\n\nCoincidentally with the discovery of neutrino oscillations, development of three dimensional\nmodels of the convective solar atmosphere [15, 16] and refinement of spectroscopic techniques\nand data (oscillator strengths of line transitions, line formation in non-local thermodynamic\nequilibrium) brought about a strong reduction in the spectroscopically determined metal abun-\ndances in the solar photosphere compared to older determinations [17, 18]. This is particularly\nimportant for the volatile and abundant carbon, nitrogen and oxygen, which added together\namount to more than 2/3 of the solar metal content. This reduction, that amounts to up to\n40% depending on the element and the authors [19], has a strong impact in solar modeling\nand puts into question the quality of SSMs as description of the solar interior properties as\ndetermined from helioseismic measurements. The apparent limitations of SSMs are evident in\nthe predicted sound speed and density profiles, depth of the convective envelope, surface helium\nabundance, small frequency separation ratios (e.g. [13, 20]) among other diagnostics. Is the\nsolar abundance problem in fact a standard solar model problem that shows the limitations of\nthis framework? If so, then it is extensive to stellar modeling in general. Does the problem\nlay in the assumptions (simplifications) entering standard solar model calculations or in the\nmicrophysics (equation of state, radiative opacities, nuclear reaction rates) employed? Is it,\non the contrary, a problem with solar abundances? If so, then it is extensive to almost all\ninferences of cosmic abundances, for most of which solar abundances are the yardstick against\nwhich they are determined. Or is the problem in our understanding of atomic physics, or of 3D\nsolar model atmospheres? All these questions are of paramount importance not only for stellar\nastrophysics and astronomy, but for any field for which cosmic abundances are a quantity of\ninterest.\n\nIn this presentation we briefly review the status of the solar abundance problem and present\nsome results of an up-to-date generation of SSMs for both high (based on 1D model atmo-\nspheres, e.g. [17]) and low (based on 3D model atmospheres, [18]) solar metallicity. Theoretical\npredictions, based on updated SSMs, show for the first time since the solar abundance problem\nappeared, that helioseimic properties of the Sun and solar neutrino fluxes reflect the same strat-\nification in the solar interior, consistent with that of high-metallicity SSMs. We argue, however,\nthat this is not necessarily an indication of a high-metallicity but rather of a well determined\ntemperature profile. Also, we discuss the limitations of the framework defined by SSMs and\nshow that, by allowing variations of the microscopic inputs within the experimentally allowed\nspace, SSMs continue to offer a good description of the solar interior. Therefore, and despite of\nthe solar abundance problem, SSM predictions are still a benchmark against which to test more\ncomplex physical descriptions of the solar interior or even to test properties of hypothetical\ncandidates to dark matter or non-standard properties of particles.\n\n2 B16: a new generation of SSMs\n\nIn 2011, a round of SSMs was published [21] that included the nuclear cross sections recom-\nmended by the then recently published Solar Fusion II review article [22]. Since then, rates\nfor some of the relevant nuclear reactions for solar models have been revised following both\ntheoretical work or a reanalysis of existing experimental data. The changes in the rates are\nnot dramatic, but Φ(8B) and Φ(7Be) solar fluxes are now measured to excellent precision\n[23, 12, 24] so even modest changes in the models translate into differences that are potentially\n\n2 Magellan Workshop 2016\n\nALDO SERENELLI, NURIA VINYOLES\n\n28 Magellan Workshop 2016\n\n\n\n0.95 0.96 0.97 0.98 0.99 1.00\n\n0.96\n\n0.98\n\n1.00\n\n1.02\n\n0.95 0.96 0.97 0.98 0.99 1.00\nΦ(7Be)/Φ(7Be)SFII\n\n0.96\n\n0.98\n\n1.00\n\n1.02\n\nΦ\n(8 B\n\n)/\nΦ\n\n(8 B\n) S\n\nF\nII SFII\n\nB16\n\nS17\n\nS11\n\nS34\n\nFigure 1: Fractional change of the SSM Φ(7Be) and Φ(8B) fluxes with respect to the previous\ngeneration of SSMs based on Solar Fusion II reaction rates.\n\nmeasurable by current and future neutrino experiments.\n\nThe most important changes have occurred for p+ p [25], 3He +4 He [26] and p+7 Be [27].\nWhile the latter only impacts the prediction of Φ(8B), the former two affect Φ(7Be) as well.\nIt is worth mentioning changes in the astrophysical factor S11(E) stem not only from changes\nin S(0) but also in S′(0) and higher order derivatives. The relative variations introduced\nby each of these rates in the fluxes are shown in Fig. 1 in black arrows while the red arrow\n\nGS98 AGSS09 Helios.\n\nαMLT 2.18± 0.05 2.11 -\nYini 0.2717± 0.0056 0.2613 -\nZini 0.0187± 0.0013 0.0159 -\nYS 0.2426± 0.0059 0.2316 0.2485\n\n±0.0035\nZS 0.0170± 0.0012 0.0134 -\nYC 0.6320± 0.0053 0.6209 -\nZC 0.0200± 0.0014 0.0159 -\n\nRCZ/R⊙ 0.7117± 0.0048 0.7224 0.713\n±0.001\n\n〈δc/c〉 0.0005+0.0006\n−0.0002 0.0021 -\n\nTable 1: Main SSM characteristics from the dif-\nferent sets of MC simulations. Also included are\nhelioseismically inferred values.\n\nshows their cumulative effect, i.e. the over-\nall change from the older generation of SSMs\n(SFII) to the new one (Barcelona 2016, B16\nin short form). These changes are common\nto all SSMs regardless the detailed composi-\ntion used to build them, so they are the same\nfor GS98 and AGSS09 solar compositions. As\nshown in the figure, B16 predictions for Φ(8B)\nand Φ(7Be) are about 4% lower than for the\nSFII SSMs. Changes in other fluxes of the\npp-chains are smaller, and well within the un-\ncertainty from neutrino experiments.\n\nHelioseismic probes are not sensitive to\nthe changes in the nuclear rates from SFII\nto the newly adopted values to a measurable\nlevel. Only S11 could introduce some changes\nbut these are too small to be of relevance. Fig. 2 shows results for the sound speed profile of\nSSMs corresponding to GS98 and AGSS09, the most widely used compositions. These are re-\nsults of two sets of Monte Carlo simulations (Vinyoles et al. in prep.) consisting of 10000 SSMs\neach. In each panel, the red curve shows the results for the central SSM, green dotted lines show\nthe 1-σ contours and the intensity of the color shade represents a histogram of the frequency of\na given δc/c occurring at a given depth r/Rsun. From the figures, it is clear that models based\non AGSS09 do not describe helioseismic inferences properly, as it has been demonstrated often\n\nMagellan Workshop 2016 3\n\nSOLAR MODELS, NEUTRINOS AND COMPOSITION\n\nMagellan Workshop 2016 29\n\n\n\n0.0 0.2 0.4 0.6 0.8 1.0\nr/Rsun\n\n−0.010\n\n−0.005\n\n0.000\n\n0.005\n\n0.010\n\n0.015\n\n0.020\n\nδc\n/c\n\nGS98\n\n0.0 0.2 0.4 0.6 0.8 1.0\nr/Rsun\n\n−0.010\n\n−0.005\n\n0.000\n\n0.005\n\n0.010\n\n0.015\n\n0.020\n\nδc\n/c\n\nAGSS09\n\nFigure 2: Histograms of sound speed profiles of SSMs where intensity indicates the frequency of\nδc/c values at given r/Rsun in the MC simulations. Red lines correspond to central values and\ngreen dotted lines represent 1-σ contours of model uncertainties. Black crosses are the sound\nspeed uncertainties propagated from uncertainties in p-mode frequencies.\n\nin the past. Table 1 offers a summary of SSM properties and uncertainties derived from the\nMC simulations. For reasons of space, errors of the AGSS09 set have not been included in the\ntable, but are quite similar to those of the GS98 MC set.\n\nGS98 AGSS09 Exper.\n\nΦ(pp) 5.99(1 ± 0.006) 6.04 5.971+0.037\n−0.033\n\nΦ(pep) 1.44(1 ± 0.01) 1.47 1.448 ± 0.013\n\nΦ(hep) 8.00(1 ± 0.30) 8.27 ≤ 19+12\n−9\n\nΦ(7Be) 4.80(1 ± 0.06) 4.38 4.80+0.24\n−0.22\n\nΦ(8B) 5.32(1 ± 0.12) 4.37 5.16+0.013\n−0.09\n\nΦ(13N) 2.78(1 ± 0.15) 2.05 ≤ 12.7\nΦ(15O) 2.05(1 ± 0.17) 1.44 ≤ 2.8\nΦ(17F ) 5.30(1 ± 0.20) 3.26 ≤ 85\n\nTable 2: Neutrino fluxes for the different B16\nSSMs with the correspondent model errors and\nexperimental results from a combined analysis\nof all neutrino experiments [24].\n\nResults confirm earlier findings, with the\nB16 set of SSMs yielding very similar helio-\nseismic results than the previous SFII gener-\nation of SSMs. Helioseismic probes of the so-\nlar interior clearly favor solar models based\nupon the GS98 composition, i.e. a high-\nmetallicity solar composition, over the more\nmodern and physically sound AGSS09 values.\nIt is important to emphasize that the avail-\nable set of helioseismic probes is not directly\nsensitive to the solar composition but to the\nactual temperature-density-pressure compo-\nsition which, in turn, depends on the radia-\ntive opacity in the solar interior. The im-\npact of metals in the solar structure is mostly\nthrough their importance as opacity sources. Therefore, helioseismic probes should be con-\nsidered dependent on the opacity profile rather than on the abundance of metals. The solar\nabundance problem still remains open.\n\nDetailed results for solar neutrino fluxes are given in Table 2. SSM results correspond\nto the MC sets and, again for reasons of space, fractional errors for the AGSS09 have been\nomitted (being very similar to those from GS98). Experimental results correspond to the\nresults of the combined analysis of all available neutrino data [24] together with the luminosity\nconstraint. Interestingly, the reduction in the predictions of Φ(8B) and Φ(7Be) in the B16\nsolar models brings the B16-GS98 results to a closer agreement with experimental results than\nits B16-AGSS09 counterpart for which now theoretical predictions are lower than experimental\nvalues. This is at odds with the previous SFII generation of models for which GS98 and\n\n4 Magellan Workshop 2016\n\nALDO SERENELLI, NURIA VINYOLES\n\n30 Magellan Workshop 2016\n\n\n\n0.7 0.8 0.9 1.0 1.1 1.2 1.3\n0\n\n2\n\n4\n\n6\n\n8\n\n10\n\n0.7 0.8 0.9 1.0 1.1 1.2 1.3\nΦ(7Be)/Φexp(\n\n7Be)\n\n0\n\n2\n\n4\n\n6\n\n8\n\n10\n\nP\nro\n\nb.\n d\n\nen\nsi\n\nty\n\nSFII−GS98\n\nSFII−AGSS09\n\nB16−GS98\n\nB16−AGSS09\n\n0.4 0.6 0.8 1.0 1.2 1.4 1.6\n0\n\n5\n\n10\n\n15\n\n20\n\n0.4 0.6 0.8 1.0 1.2 1.4 1.6\nΦ(8B)/Φexp(\n\n8B)\n\n0\n\n5\n\n10\n\n15\n\n20\n\nP\nro\n\nb.\n d\n\nen\nsi\n\nty\n\nSFII−GS98\nSFII−AGSS09\nB16−GS98\nB16−AGSS09\n\nFigure 3: Comparison of solar neutrino fluxes determined from all available neutrino data (black\nlines [24]) and the previous (SFII - dotted lines) and current (B16 - thick solid lines) generations\nof SSMs.\n\nAGSS09 compositions led to solar models that had Φ(8B) and Φ(7Be) fluxes in practially\nequal agreement with experimental data [21].\n\nThe new situation is more clearly illustrated in Fig 3 where model and experimental results\nare compared. Fluxes are normalized to experimental values [24]. Results from the older\nSFII SSMs are shown in dotted lines and, for GS98 and AGSS09 compositions, are almost\nsymmetrically located to each side of the experimental result of the corresponding flux, Φ(8B)\nor Φ(7Be). With the B16 models, in solid lines, experimental results clearly show a preference\nfor the GS98 SSM. A detailed quantification of the level of agreement of each of the B16 SSMs\nis ongoing work (Song et al. 2016 in prep.).\n\nAs stated above, helioseismic probes have a strong preference for GS98 based SSMs, although\nas also mentioned this cannot be interpreted as direct measurement of the solar composition\nbut rather an indirect determination of the opacity profile in the solar interior [28, 29]. For\nthe SFII models, it was not possible to claim that neutrino experiments had a preference of\nmodels of a given composition. Now, however, this situation has changed and for the B16 set of\nmodels solar neutrino fluxes also show the same results: GS98-based SSMs are to be preferred.\nFor the first time solar neutrinos and helioseismology favor the same solar interior properties,\nconsistent with those inferred from high metallicity solar models.\n\nIt is important to realize that neutrino fluxes originating in any of the pp-chain reactions\nare sensitive also to the temperature stratification in the solar core. This is to say, they are\nnot directly sensitive to the composition but rather to the opacity profile. The situation is\ntherefore very much the same as for the helioseismic probes: the sensitivity lies in the opacity\nprofile, not the composition. For any of these probes, there is a degeneracy between opacity\nand metal abundances that needs independent constraints to be broken. At this moment, it\nis not possible to state whether AGSS09 metal abundances are too low or all atomic opacity\ncalculations are intrinsically flawed and underestimate the actual radiative opacity in solar (and\nstellar) interiors.\n\nRecently, the first ever experimental measurements of iron opacity under conditions quite\nclose to those found at the base of the convective envelope of the Sun have become available [30].\nThese results seem to indicate that, in fact, opacity calculations [31, 32] underestimate the true\niron opacity by large fractions that result in a final Rosseland mean opacity (the one actually\nrelevant in solar interior calculations) that is underestimated by about 7% at the base of the\n\nMagellan Workshop 2016 5\n\nSOLAR MODELS, NEUTRINOS AND COMPOSITION\n\nMagellan Workshop 2016 31\n\n\n\nconvective envelope. While clearly not enough to bring AGSS09 models into agreement with\nhelioseismic data because an increase of the order of 15 to 20% is needed, these results are the\nfirst indication that in fact atomic opacity calculations might need a thorough revision. Some\nwork in this direction has been carried our recently [33], showing that the treatment of line\nbroadening alone may introduce an uncertainty level of several percents in opacity calculations.\n\n3 Best-fit SSM\n\nIn the standard framework of SSM calculations the number of free parameters is kept to a\nminimum and phenomenological calibrations are avoided as much as possible (see e.g. [34]\nfor more details). This strategy ensures that SSMs the recipe with which they are computed,\nbeing well defined, yields robust predictions. But, at the same time, it is clear that physical\nphenomena are modeled poorly in SSMs (e.g. near surface convection) or not modeled at all\n(e.g. rotation). It is also well known SSMs cannot account for some observations, e.g. the solar\nsurface lithium abundance (more then 2 orders of magnitude lower than found in meteorites)\namong others.\n\nA question that can be asked is: what are the limits of the SSM framework? Or, posed\ndifferently, what is the best possible description of solar interior properties the SSM framework\ncan offer? The answer is important for several reasons. Among others: SSMs are used to set\nlimits to exotic particles or non-standard properties of standard particles, SSM framework is the\nsame entering most of stellar structure and evolution models, SSMs are the reference against\nwhich non-standard stellar physics is tested.\n\nFigure 4: Sound speed profiles as in Fig. 2. The best-fit SSM (see text for details) is shown in\nblack. The red and grey shaded area show the 1-σmodel and sound speed inversion uncertainties\nrespectively.\n\nA way of answering this question is to allow input parameters (nuclear reaction rates, com-\nposition, opacity profile, etc.) in the SSMs as quantities that can be varied to provide the\nbest possible fit to combined helioseismic and solar neutrino data. This exercise was carried\nout initially by [29], where a global χ2 function was built using 32 helioseismic constraints (30\npoints of the sound speed profile, depth of convective envelope, surface helium abundance) and\nthe Φ(8B) and Φ(7Be) neutrino fluxes. The response of each of these quantities to changes\nin the input parameters was built using power-law expansions and the input parameters were\n\n6 Magellan Workshop 2016\n\nALDO SERENELLI, NURIA VINYOLES\n\n32 Magellan Workshop 2016\n\n\n\nallowed to vary around their measured values by introducing the concept of pulls with a penalty\nfunction in the global χ2. χ2 is then minimized with respect to these pulls to offer a set of\nvaried input parameters that allow the reconstruction of a best-fit SSM.\n\nLater, the idea was extended to produce bounds for properties of exotic particles (axions\nand dark photons) [35]. In this approach, the solar composition is allowed to vary freely (no\npenalty). In the case of SSMs, the resulting sound speed profile of the best-fit SSM is shown in\nFig. 4, where it is compared to those from GS98 and AGSS09 SSMs. As it can be seen in the\nfigure, the performance of the GS98 model can be improved if input parameters in the SSM\nare varied (usually within 1-σ) from the currently accepted values. Of course, the best-fit SSM\nreflects a solar composition close to that of GS98. The idea that SSMs can be used in studies\nof particle physics, regardless of the unsolved solar abundance problem, then relies on the fact\nthat the interaction of these particles with standard matter (e.g. magnetic dipole of neutrinos),\nor the energy loss rates (in the case of weakly interacting particles), does not depend (or does so\nvery weakly) on the detailed composition of the solar interior. 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Palmeri, C. J. Zeippen, and\nM. J. Seaton. Up-dated opacities from the Opacity Project. Mon. Not. Roy. Astron. Soc., 360:458–464,\n2005.\n\n[33] M. Krief, A. Feigel, and D. Gazit. Line Broadening and the Solar Opacity Problem. Astrophys. J., 824(1):98,\n2016.\n\n[34] Aldo Serenelli. Alive and well: a short review about standard solar models. Eur. Phys. J., A52(4):78, 2016.\n\n[35] Núria Vinyoles, Aldo Serenelli, Francesco L. Villante, Sarbani Basu, Javier Redondo, and Jordi Isern. New\naxion and hidden photon constraints from a solar data global fit. JCAP, 1510(10):015, 2015.\n\n8 Magellan Workshop 2016\n\nALDO SERENELLI, NURIA VINYOLES\n\n34 Magellan Workshop 2016\n\n\n\nCNO neutrinos and metallicity of stars\n\nStefano Davini1\n\n1Gran Sasso Science Institute, L’Aquila AQ 67100, Italy\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/25\n\nThe detection of neutrinos resulting from the CNO cycle would be the first direct evidence\nof the nuclear process that is believed to fuel massive stars. A precise measurement of the\nCNO solar neutrino fluxes would also help resolving the solar metallicity problem, as the\npredicted fluxes strongly depend on the inputs of the solar modelling. This articles, part\nof the Magellan 16 proceedings, reviews the connections between CNO neutrinos and solar\nmetallicity, and the experimental attempts to detect them.\n\n1 Introduction\n\nCNO neutrinos are the neutrinos emitted in the CN and NO bi-cycle, one of the two set of\nfusion reactions in the Sun. The reactions in the CNO bi-cycle which involves the emission of\nneutrinos are the following:\n\n13N → 13C+ e+ + νe (1.19MeV), (1)\n15O → 15N+ e+ + νe (1.73MeV), (2)\n17F → 17O+ e+ + νe (1.74MeV), (3)\n\nwhere the energy in parenthesis is the kinetic energy available to the final states of the reactions.\nThe relevance of the CNO bi-cycle to the total luminosity of the Sun is estimated to be of the\norder of ∼ 1%: the contribution to the energy budget of the CNO reactions is marginal in Sun-\nlike stars and therefore the flux of the CNO neutrinos is low, in the ballpark of one hundredth\nof the pp solar neutrino flux, and hard to determine experimentally.\n\nNevertheless, a precision measurement of the CNO neutrino flux would be of paramount\nimportance in astrophysics: the accuracy of the physical description of the Sun provided by\nSolar Standard Models (SSMs) has been challenged by developments in stellar spectroscopic\ntechniques over the last decade. An experimental determination of CNO neutrino fluxes is likely\nthe only probe to settle the challenge.\n\nThe accuracy of the description of the physics of the Sun is critical because of the fun-\ndamental role that Solar Standard Models play in our understanding of the Universe. The\nSolar Standard Model is the calibration input for stellar evolution and plays a fundamental\nrole in cosmology. Examples of the role of the SSM as source of calibration in astrophysics and\ncosmology are the following: convection models in stars are calibrated forcing solar models to\nreproduce present day solar radius and temperature, the evolution of metals and helium in the\nUniverse needs input from both Big Bang Nucleosynthesis and initial SSM composition, the\nSSM is used as a benchmark against additional physics processes in stars.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 35\n\n\n\n2 The Solar abundance problem\n\nThe agreement between solar models and helioseismology has been altered after new spectro-\nscopic determinations of the photosphere composition [1]. The new determinations were ob-\ntained with an advanced three-dimensional hydrodynamic model instead of a one-dimensional\nmodel, better atomic and molecular data, and non local thermodynamic equilibrium (NLTE)\ncalculations. Using the new input, the abundances of C, N, O in the Sun are lowered by 20-\n30%. The new abundances are challenging for the SSM, because the previous agreement with\nhelioseismic data is lost.\n\nDoes this disagreement question the validity of the Solar Standard Models or stellar models\nin general? The answer is not easy. ”It seems clear that the low metallicity solar abundances\nare here to stay”, says Serenelli [2]. A good consequence of the Solar abundance problem is that\nit motivated further work on solar models, such as nuclear reaction rates, radiative opacities,\nand state equations.\n\nSolar opacities and CNO neutrinos play a major role in the Solar abundance problem. A\nsuitable change of solar opacity profile produces same effects on helioseismic observable and\nneutrino fluxes of a change of solar composition, except for CNO neutrinos. CNO neutrinos\ncan thus break the degeneracy between solar abundances and radiative opacities.\n\n3 Solar neutrinos and solar abundancies\n\nThis section is an informal review of the experimental determination of the solar neutrino fluxes\nand what the determinations can tell us about the Sun. Both the experimental determination\nand SSM predictions are subject to rapid changes over time, so the values reported here are\ntruncated at the first significant digit or given as order of magnitude. Any reader interested in\nmore digits can find them in the references.\n\nThe solar neutrino detectors which are operative at present day are Borexino [3], 300 ton\n(fiducial) liquid scintillator detector at Laboratori Nazionali del Gran Sasso (LNGS) in Italy,\nand Super-Kamiokande [4], 22.5 kton (fiducial) water Cherenkov detector at the Kamioka mine\nin Japan. Borexino is engaged in the spectroscopy of solar neutrinos, which is possible due\nto the low threshold (as low to detect pp neutrino recoils on electrons) and the ultra-pure\ndetector conditions. Super-Kamiokande is exploiting its big mass and directionality capabilities\nto perform a precision measurement the flux solar neutrinos which recoils on electrons with\nkinetic energy above 3.5MeV, which are essentially 8B neutrinos (the flux of hep neutrinos is\nto low to allow for a detection at present time).\n\nThe neutrinos emitted in the pp and pep reactions are the ones whose fluxes are predicted\nwith the highest accuracy by the SSM, because of the luminosity constraint. The uncertainty of\nthe pp flux prediction in the SSMs is below the per-mille level, while the uncertainty of the pep\nflux is at the percent level. The neutrino fluxes from these reactions are not directly sensitive to\nthe Solar abundances, but to the radiative opacities. The flux of pp and pep reactions is indeed\nmostly driven by the core temperature, therefore to the radiative gradient determined by the\nopacity. Neutrinos from pp and pep reactions are not good probes for the Solar abundances,\nbut they may be excellent probes of the core temperature if their flux is measured with enough\nprecision, and they provide a test of the solar luminosity.\n\nThe experimental accuracy is about 10% for pp neutrinos (Borexino, [5]) and about 25% for\npep neutrinos (Borexino, [6]). The determination of the pp neutrino interaction rate in Borexino\n\n2 Magellan Workshop 2016\n\nSTEFANO DAVINI\n\n36 Magellan Workshop 2016\n\n\n\nis challenging because of the backgrounds due to of 14C. The determination of the pep neutrino\ninteraction rate in Borexino is challenging because of the low statistics (few events per day in\nthe region of interest and the fiducial volume) and the backgrounds due to the cosmogenic 11C\nand the radiogenic 210Bi.\n\n7Be and 8B solar neutrinos are emitted in the proton-proton chain, therefore their fluxes\nare subject to the same degeneracy between the core temperature (then radiative opacity) and\nthe Solar abundances described above. The experimental determination of the 7Be and 8B\nsolar neutrino flux is more precise than their theoretical counterpart in the SSMs. The best\nmeasurement of the 7Be solar neutrino flux is from Borexino [7], with an accuracy of about 5%.\nThe uncertainty in the SSM is about 7%. The best measurement of the 8B solar neutrino flux\nis from Super-Kamiokande [8], with an accuracy of about 3%. The uncertainty in the SSM is\nabout 14%.\n\n8B solar neutrinos deserve a few more sentences of merit, even if a bit off-topic. The\nmeasurement of the 8B solar neutrino interaction rate in the SNO experiment, exploiting both\nneutral current (sensitive to all neutrino flavors) and charged current reactions (sensitive to\nelectron neutrino only) in in D2O, firmly established the flavor conversion of solar neutrinos.\nArt Mc. Donald, spokesperson of SNO, was awarded half of the Nobel prize in 2015, and\nthe other half was awarded to Takashi Kajita of Super-Kamiokande for the discovery of the\natmospheric neutrino oscillation in Super-Kamiokande.\n\nClosing this off-topic paragraph on neutrino oscillation and history of physics, we return to\nCNO neutrinos. The temperature profile in the solar core is established by the proton-proton\nchain (and not by the CNO bi-cycle), therefore the experimental measurement of CNO neutrino\nflux may be used to probe the Solar abundances, being the flux linear in the abundance of carbon\nand nitrogen. The only experimental limit on the CNO neutrino flux is from Borexino [6].\nThe limit is about 40% higher than the high metallicity SSM predictions, therefore the Solar\nabundances of metals are basically unconstrained.\n\n4 The challenges of CNO neutrino detection\n\nIt should be clear from the previous sections that CNO neutrinos are the perfect probe to\ndetermine the solar abundances. The goal should be clear: measure the CNO neutrino fluxes\nto get the abundances. Why this has not been done? Because an experimental determination\nof the CNO neutrino flux is extremely, extremely, challenging.\n\nThe reason why CNO neutrinos are the best probe is also one of the reason why they are so\nhard to detect. The solar luminosity and the temperature in the core are driven by the proton-\nproton reactions, with the CNO bi-cycle reactions being a marginal source of energy in the Sun.\nThis means that the CNO neutrino flux can discriminate between the high metallicity and low\nmetallicity SSM models, breaking the degeneracy with the radiative opacity. But this also means\nthat the CNO neutrino fluxes from the Sun are low, 3 –5 · 108 cm−2 s−1, at the level of 1% of\nthe pp neutrino flux. The low flux alone would be not so much of a problem: 8B neutrinos\nare even lower in flux (a factor hundred less), but many of their recoils have high enough\nenergy to be detectable even with water Cherenkov detectors, at energy above backgrounds\nfrom radiogenic and cosmogenic radioactivity. The Nature is not so kind with CNO neutrinos:\nthe CNO energy spectrum falls below 2MeV, and this means being overwhelmed by natural\nradioactivity backgrounds.\n\nEven if a large fraction of the CNO neutrino spectrum is above the Cherenkov emission\n\nMagellan Workshop 2016 3\n\nCNO NEUTRINOS AND METALLICITY OF STARS\n\nMagellan Workshop 2016 37\n\n\n\nthreshold in water, CNO neutrinos are well below the experimental threshold of the water\nCherenkov detector Super-Kamiokande (about 3.5MeV). The only detector type available today\nfor detecting CNO neutrinos are ultra-pure organic liquid scintillators. Borexino at LNGS is\nthe most radio-pure detector between 200keV and 2MeV, among the detectors operated at\npresent time and in the past. Even Borexino could only provide limits to the CNO neutrino\nflux, because of the residual radioactive backgrounds and the correlation with pep neutrinos in\ndetermining the fluxes with fit to the energy spectrum.\n\nThe main radioactive backgrounds in Borexino, which any future organic liquid scintillator\ndetector for CNO neutrinos will have to fight, are the radiogenic 210Bi and the cosmogenic 11C.\n\n210Bi is a β− emitter with end point 1.16MeV and five days half-life. The real problem\nis that the spectral shapes of 210Bi and electron recoils from CNO neutrinos are very similar\nand do not show distinct features like peaks. 210Bi is part of the 238U chain, its predecessor is\n210Pb and its daughter is 210Po. 210Bi and 210Po are found not to be in secular equilibrium,\ntherefore constraining the activity of 210Bi from the rate of 210Po (which is easier to measure)\nis not trivial (but not impossible in principle, as shown in [9]). It is particularly challenging to\nremove 210Bi by purification methods [10]; it is indeed the only radiogenic β− emitter in the\nphase-II of Borexino whose activity is above 10 decays per day per 100 tons (14C is excluded\nin this evaluation).\n\n11C is a cosmogenic radioactive isotope, generated in the inelastic scattering of muons on\n12C, µ+11C → µ+12C+n. 11C is a β+ emitter with end point 1.98MeV (including the energy\nfrom the annihilation of the electron-positron pair) and 20 minutes half-life. The background\ndue to cosmogenic isotopes can be of course reduced by increasing the detector overburden.\nAnother way to reduce the background is to track the muon and locate the neutron capture,\nthen apply space-time cuts to exclude regions of the detector where it is more likely that 11C\nwas produced (and decays), like it was done in Borexino [11]. This method however sacrifices\nsubstantial fractions of the total exposure. Also, it is possible to constrain 11C by exploiting\nthe slightly different scintillation pulse shape for β− and β+ decay in liquid scintillators, mainly\ndue to ortho-positronium formation [12].\n\n5 The ideal CNO hunter\n\nIt should be clear to the reader that hunting CNO neutrinos is not an easy task. I will review\nhere the features that a CNO neutrino must have in order to have some chances to perform a\nmeasurement.\n\nThe detector must be large. The ideal scale is kiloton. The CNO neutrino fluxes and\ncross sections are low, therefore the detector should be large enough to collect a relevant number\nof CNO interactions. Large detectors also allow for better fiducialization, which is important\nbecause the low energy tails of the external background (due mainly to 208Tl and 214Bi γ-rays)\nmust be negligible around the CNO neutrino endpoint.\n\nHigh energy resolution. It is of paramount importance to discriminate the CNO neutrino\nelectron recoils from the radioactive background and the residual solar neutrinos. In particular,\nthe energy spectrum of 210Bi decay and CNO neutrino electron recoils are very similar, and\nthe risk is not being able to resolve the two components if the resolution is not high enough.\nAlso in absence of any radioactive background (a dream detector), the spectrum due to CNO\nneutrinos must be identified below the spectrum of 7Be and pep neutrinos.\n\n4 Magellan Workshop 2016\n\nSTEFANO DAVINI\n\n38 Magellan Workshop 2016\n\n\n\nUltra-pure detector. In particular, the levels of 222Rn, 210Pb, 210Bi, and 210Po must be\nultra-low. 210Bi should be below the CNO interaction rate, which in Borexino is predicted to\nbe 5 (3) decays per day per 100 tons in the high (low) metallicity SSM. Consider that it is\nsufficient to remove the contamination of 210Bi to place limits on the CNO neutrino flux, but\nit is necessary to estimate the residual 210Bi activity in the detector to convincingly measure\nthe CNO neutrino flux.\n\nDeep detector. Cosmogenic backgrounds must not be an issue. It is true that most\nof cosmogenic decays can be removed by space-time cuts, but these cuts usually remove a\nsubstantial fraction of the detector exposure.\n\nStable detector. In order to measure CNO neutrinos, the residual radioactive backgrounds\nin the detector must be known. The radioactive backgrounds should be stable in space in or-\nder to be measured with enough accuracy. In particular, there must be no convection moving\nradioactive contaminants around the detector and changing their concentration over time. Ex-\nperiments should consider thermal insulation to prevent or decrease convective motions due to\ntemperature changes in the experimental halls.\n\nParticle identification. Discriminating α backgrounds from β backgrounds allows to tag\n210Po, 212Bi-Po, and 214Bi-Po, making it possible to evaluate the activity of branches in the 238U\nand 232Th chains, and investigate if the secular equilibrium condition is met. Discrimination\nof β radioactivity from γ radioactivity may be helpful to reduce external background and β+\n\ndecays.\nDirectionality. Measuring the direction of recoil electrons, which is correlated to the\n\nincoming direction of neutrinos, would be tremendously helpful and nihil the relevance of mostly\nall internal radiogenic backgrounds. Unfortunately, today there is no demonstrated directional\ndetection technology for neutrino recoils at 1MeV, scalable to kiloton masses.\n\n6 Future CNO hunters\n\nAmong experiments under construction, or ready to be approved, or proposed, there are few\nthat may be good CNO hunters. This list is not exhaustive by purpose (and the space in\nthis proceeding has a upper bound), therefore nobody should feel offended if their favorite\nexperiment is not in the list.\n\nSNO+ at SNOLab is a organic liquid scintillator detector with the advantages of being\nlarge (1 kton) and deep (the 11C production rate should be reduced of a factor hundred respect\nto Borexino at LNGS). SNO+ may start a solar neutrino campaign after the neutrino-less\ndouble beta decay (in 130Te) phase.\n\nThe JINPING Neutrino Experiment is a proposed organic liquid scintillator detector\nwith the advantages of being even larger (20 kton) and even deeper (China JinPing Laboratory-\nII, already under construction, is going to be the deepest underground laboratory) [13]. In the\nletter of intent, one can read ”We predict a capacity to discover solar neutrinos from the\ncarbon-nitrogen-oxygen (CNO) cycle with more than 5 sigma of statistical significance”.\n\nARGO at LNGS is a 200 ton fiducial two-phase liquid argon time projection chamber\n(LAr TPC) for direct dark matter WIMP searches [14]. LAr TPCs have the advantages of\nhaving higher Z and better scintillation light yield and resolution of organic liquid scintillators.\nSimulations in [14] show that ”such a detector could measure the CNO neutrino rate with ∼15%\nprecision”.\n\nSolar neutrinos are going to be a background for the next (or the next to next) generation\n\nMagellan Workshop 2016 5\n\nCNO NEUTRINOS AND METALLICITY OF STARS\n\nMagellan Workshop 2016 39\n\n\n\nof direct dark matter WIMP experiment. The reader should not be surprised of a possible\ngrowing interest in solar neutrino detection over the next decade(s).\n\n7 Conclusion and outlook\n\nThere is a solar abundance problem. And it is likely that the new solar abundances are here\nto stay. The experimental measurement of the flux of neutrinos from the CNO bi-cycle can\nsettle the problem. However, the experimental detection of CNO neutrinos is challenging. Some\nfuture projects may be able to detect CNO solar neutrinos.\n\nAknowledgments\n\nI gratefully acknowledge the Organizers of Magellan 16 for the invitation.\n\nAppendix A: Multidisciplinar workshop\n\nMultidisciplinar workshops, like Magellan, are indeed very useful to strengthen the bonds be-\ntween astrophysicist and particle physicists. Recalling a famous quotation from Feynman, even\nif we (or academia) split the topics in different subjects, the Nature does not.\n\nA problem with multidisciplinary is that sometimes the lack of a common background is\nhuge. Question time is very useful construct bridges over this gap. In the question time, I\nwas asked if it is possible to detect neutrinos from other stars besides the Sun. The answer is\nno, except SuperNovae neutrinos. To convince yourself, you can try to compute the number of\nneutrino interactions detected in one year in a Borexino-like detector (Borexino detects about\n200 neutrinos per day per 100 tons) from a Sun-like star one light year from us.\n\nReferences\n[1] M. Asplund et al., Annual Review of Astronomy & Astrophysics, 47 481 (2009).\n\n[2] A. Serenelli, Eur. Phys. J. A 52 78 (2016).\n\n[3] G. Alimonti et al., Nucl. Instr. Meth. A A600 568-593 (2009).\n\n[4] Y. Fukuda et al., Nucl. Instr. Meth. A A501 418-462 (2003).\n\n[5] G. Bellini et al., Nature 512 7515, 383-386 (2014).\n\n[6] G. Bellini et al., Phys. Rev. Lett 108 051302 (2012) .\n\n[7] G. Bellini et al., Phys. Rev. Lett 107 141302 (2011).\n\n[8] K. Abeet al., arXiv:hep-ex/1606.07538 (2016).\n\n[9] F.L. Villante et al., Phys. Lett. B701 336 (2011).\n\n[10] G. Alimonti et al., Nucl. Instr. Meth. A A609 58-78 (2009).\n\n[11] G. Bellini et al., Phys. Rev. D89 112007 (2014).\n\n[12] D. Franco et al., Phys.Rev. C83 0105504 (2011).\n\n[13] J.F. Beacom et al., arXiv:physics,ins-det/1602.01733 (2016).\n\n[14] D. Franco et al., arXiv:physics,ins-det/:1510.04196 (2015).\n\n6 Magellan Workshop 2016\n\nSTEFANO DAVINI\n\n40 Magellan Workshop 2016\n\n\n\nThe GERDA Experiment: Search for the Neutri-\n\nnoless Double Beta Decay\n\nMichael Miloradovic1 on behalf of the Gerda Collaboration\n\n1Physik Institut der Universität Zürich, Zürich, Switzerland\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/34\n\nThe Germanium Detector Array (Gerda) experiment is searching for the neutrinoless dou-\nble beta decay of 76Ge. The observation of this Beyond the Standard Model process would\nprove the existence of a neutrino Majorana mass component and provide information on\nthe neutrino mass hierarchy and absolute mass scale. Gerda operates enriched germanium\ndiodes, acting simultaneously as the source and detector material, directly submerged in\nliquid argon. Phase I achieved the world’s best lower limit of T 0νββ\n\n1/2\n> 2.1 · 1025 yr (90%\n\nC.L.). With the recent completion of the upgrade to Phase II, an additional 20 kg of\ngermanium detectors – for a total of 35 kg – and a liquid argon veto system have been\nimplemented. The goal is an order of magnitude lower background with a projected sen-\nsitivity of 1.4 · 1026 yr for T 0νββ\n\n1/2\n.\n\n1 Matter antimatter asymmetry\n\nToday, our universe is completely matter dominated. All galaxies and stars consist of baryons.\nAntimatter exists only as a product of high-energy particle collisions. In the early universe, this\nwas not the case. According to the Standard Model of Particle Physics, the Big Bang should\nhave created a completely symmetrical universe with equal amount of matter and antimatter.\nIf this thought is developed further, the quark and antiquark pairs would interact in pairs\nleading to the complete annihilation of this symmetrical universe. In reality, a slight asymmetry\nintroduced in the early stages of the universe is responsible for the small excess of matter in\ncomparison to antimatter that now makes up our universe. The origin of this asymmetry\ncould lie in neutrino nature. Determining the nature of the neutrinos is thus of fundamental\nimportance in modern physics [1].\n\n2 Neutrinos\n\nIn 1937, Ettore Majorana suggested that the neutrino could be its own antiparticle [2]. The\nrecent discovery of neutrino oscillations establish the non-zero mass of neutrinos [3, 4]. The\nmatter antimatter asymmetry could be explained through an extension of the Standard Model,\nwherein the see-saw mechanism introduces right-handed neutrinos. CP violating decays of\nthese particles then spontaneously generate leptons resulting in a lepton asymmetry in the\nearly universe. Baryogenesis via leptogenesis could thus lead to the observed matter antimatter\n\nMagellan Workshop 2016 1Magellan Workshop 2016 41\n\n\n\nasymmetry [5]. The prime avenue to directly probe this fundamental characteristic and simul-\ntaneously obtain information on the absolute neutrino masses is the neutrinoless double beta\ndecay. This postulated decay channel could involve two Majorana neutrinos, which annihilate\noff-shell in a lepton number violating interaction with zero neutrinos in the final state.\n\n3 The GERDA experiment\n\nThe Germanium Detector Array (Gerda) experiment is searching for the neutrinoless double\nbeta decay of 76Ge. It is located underground in Hall A of the Laboratori Nazionali del Gran\nSasso (LNGS) with a 1400 m rock overburden. The isotopically enriched germanium diodes\nare directly submerged inside a 64 m3 high purity liquid argon cryostat and act simultaneously\nas the source and detector material. Surrounding the cryostat, a 10 m diameter purified water\ntank shields the experiment and serves as a Cherenkov muon veto featuring 66 photomultiplier\ntubes (PMTs). These shielding and veto layers ensure a substantial reduction of the background\nand the highest possible sensitivity for the experiment [6].\n\nFigure 1: The Gerda experiment with its germanium detector array directly submerged in a\nliquid argon cryostat, surrounded by a water tank Cherenkov muon veto.\n\nThe germanium detectors offer an excellent energy resolution of ∼ 0.15% FWHM at Qββ.\nAnother advantage is the possibility of Pulse Shape Discrimination (PSD), wherein the induced\nsignal shapes are used to further reduce the background by identifying single site events (e.g.\n\n2 Magellan Workshop 2016\n\nMICHAEL MILORADOVIC\n\n42 Magellan Workshop 2016\n\n\n\n0νββ), multi site events (e.g. Compton scattered γ), and surface events (e.g. α/β events).\n\nBackground reduction techniques are crucial as the sensitivity on the half-life of the neutri-\nnoless double beta decay strongly depends on the background index (BI) as follows\n\nT0ν\n1/2 ∝\n\n√\nM · t\n\n∆E ·BI\n. (1)\n\nOnly in a zero background regime can a proportional scaling to the exposure (M · t) be attained\nsuch that\n\nT0ν\n1/2 ∝ M · t. (2)\n\n4 GERDA Phase I\n\nIn Gerda Phase I, running from November 2011 to May 2013, 15 kg of semi-coaxial and 3 kg\nof Broad Energy (BEGe) germanium detectors were used on four strings for a total exposure of\n21.6 kg · yr. A tenfold lower background in comparison to previous experiments was obtained\nwith a background index of around 1 · 10-2 counts/(keV · kg · yr) at Qββ, fulfilling the design\ngoal. Figure 2 shows the best fit background model including the individual contributions,\nperformed before unblinding a 40 keV window around Qββ [7].\n\nFigure 2: Phase I background best fit minimum model and individual contributions. The data\ninside the blinded window (UB, light grey) of size 40 keV is excluded from the fit. Figure\nfrom [7].\n\nWith this progress, it was possible to achieve the world’s best lower limit of T 0νββ\n1/2 >\n\n2.1 · 1025 yr (90% C.L.) for the half-life of the neutrinoless double beta decay of 76Ge. Figure 3\ndepicts the region of interest, the relevance of the PSD, and the lower limit [8].\n\nMagellan Workshop 2016 3\n\nTHE GERDA EXPERIMENT: SEARCH FOR THE NEUTRINOLESS DOUBLE BETA DECAY\n\nMagellan Workshop 2016 43\n\n\n\nFigure 3: The results of Gerda Phase I in the region of interest (with and without PSD),\n\nleading to the lower limit of T 0νββ\n1/2 . Figure from [8].\n\n5 GERDA Phase II\n\nFor Phase II of the experiment, the active mass of germanium diodes is increased by an addi-\ntional 20 kg of BEGe detectors for a total of 40 detectors on 7 strings. The BEGe detectors\noffer superior PSD capabilities. A liquid argon hybrid veto system was introduced through the\ninstallation of PMTs and an optical fibre curtain coupled to silicon photomultipliers (SiPMs)\ninside the cryostat. The detection of liquid argon scintillation light is used to reject external\nbackground events. Combining all developments, the background can thus be efficiently reduced\nby an order of magnitude. The goal is to achieve a median sensitivity of T 0νββ\n\n1/2 ∼ 1.5 · 1026 yr\n\nfor a total exposure of 100 kg · yr. Table 1 summarises the Phase I and the aim of Phase II [9].\nThe upgrade to Gerda Phase II has been finished and the data taking has begun in December\n2015.\n\nPhase Active Mass BI T 0ν\n1/2\n\n[kg] [counts/(keV·kg·yr)] Sensitivity [yr]\n\nI (finished) 15 10-2 2.1 · 1025\nII (expected) 35 10-3 1.4 · 1026\n\nTable 1: Gerda Phase I results and Phase II goals in terms of the active detector mass,\nbackground index and median sensitivity on the 76Ge neutrinoless double beta decay half-life.\n\n4 Magellan Workshop 2016\n\nMICHAEL MILORADOVIC\n\n44 Magellan Workshop 2016\n\n\n\nReferences\n[1] K. Nakamu and S. Petcov, Particle Data Group Review 2014, PDGRPP (2014).\n\n[2] E. Majorana, Teoria simmetrica dellelettrone e del positrone. Il Nuovo Cimento 14 171 (1937).\n\n[3] Y. Fukuda et al., Phys. Rev. Lett. 81 1562 (1998).\n\n[4] Q. R. Ahmad et al., Phys. Rev. Lett. 87 071301 (2001).\n\n[5] M. Fukugita and T. Yanagida, Phys. Lett. B174 45 (1986).\n\n[6] The Gerda Collaboration, Eur. Phys. J. C73 2330 (2013).\n\n[7] The Gerda Collaboration, Eur. Phys. J. C47 2764 (2014).\n\n[8] The Gerda Collaboration, Phys. Rev. Lett. 111 122503 (2013).\n\n[9] K. T. Knöpfle for the Gerda Collaboration, PoS 109 (TIPP2014).\n\nMagellan Workshop 2016 5\n\nTHE GERDA EXPERIMENT: SEARCH FOR THE NEUTRINOLESS DOUBLE BETA DECAY\n\nMagellan Workshop 2016 45\n\n\n\n\n\nThe atmospheric muon charge ratio: a probe to\n\nconstrain the atmospheric νµ/ν̄µ ratio\n\nNicoletta Mauri\n\nINFN - Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/11\n\nThe atmospheric muon charge ratio, defined as the number of positive over negative charged\nmuons, is an important observable to shed light on the physics of cosmic ray interactions\nin atmosphere. It allows studying the features of high-energy hadronic interactions in the\nforward region and the composition of primary cosmic rays. In particular, the TeV muon\ncharge ratio provides sensitivity to the charge ratio of high energy kaons, the principal\nparents of atmospheric neutrinos. In this paper the results from the OPERA experiment\nin the TeV energy range are reviewed.\n\n1 Introduction\n\nThe atmospheric muon charge ratio Rµ ≡ Nµ+/Nµ− is studied and measured since many\ndecades over a wide muon energy range, from few GeV up to several TeV. It provides relevant\ninformation on both cosmic rays and particle physics through its dependence on several aspects:\nprimary chemical composition and energy spectrum, hadronic interactions features and, at very\nhigh energy, production and prompt decays of charmed particles.\n\nAtmospheric muons constitute the penetrating charged remnants of cosmic rays under the\nEarth’s surface. They are produced when primary cosmic rays, typically protons, impinge on\nthe Earth’s atmosphere starting a particle cascade, in which secondary particles decay into\nmuons (and muon neutrinos):\n\nN + air→ π±,K±(, D0, D±,ΛC . . .)→ µ± (−)\nν µ\n\nIn the energy range up to ∼100 GeV atmospheric muons come mostly from the decay of sec-\nondary pions. At higher energies, the kaon contribution to the muon flux increases, reaching\nthe asymptotic value of 27% at about 10 TeV [1]. The pion and kaon components constitute\nthe conventional muon flux. At even higher energies, at O(100) TeV, also charmed hadron\ndecays are expected to contribute (prompt muon component).\n\nAtmospheric muon neutrinos share the mesonic origin with atmospheric muons, π±,K± →\nµ± νµ(ν̄µ), but the kaon and pion contributions are quite different due to the kinematics of\ntheir two-body decays. The muon carries most of the energy in pion decay (mπ & mµ), so pions\nare the dominant parents for atmospheric muons (Fig. 1, left panel). Due to the different kaon\ndecay phase space (mK � mµ), kaons are more efficient than pions in producing neutrinos and\ntheir contribution is ∼ 80% above O(1) TeV (Fig. 1, right panel).\n\nMagellan Workshop 2016 1Magellan Workshop 2016 47\n\n\n\nAnatoli Fedynitch, CR+LHC workshop, CERN, 12.02.2012\n\n• The steepness of the primary spectrum suppresses the contribution of \nsecondary interactions (SI) of the form                                 or N ! K ! ⇡ ! µ\nN ! ⇡ ! ⇡ ! µ\n\n• The combination of meson abundance, their decay length, the density of the \natmospheric profile,the branching ratios and the decay kinematics results in \ndifferent meson origin of the observed lepton species\n\nMesonic origin\n\n13Figure 1: Conventional contributions to atmospheric muon and muon neutrino fluxes as a\nfunction of energy [2]. The steepness of the primary spectrum suppresses the contribution of\nsecondary interactions (SI).\n\nThe results on the atmospheric muon spectrum and charge ratio are essential for a precise\nprediction of the atmospheric neutrino flux. This is needed both in the low energy range (from\nMeV to TeV) for detailed measurements of neutrino oscillations and mass hierarchy in atmo-\nspheric neutrino experiments and in the high energy range (from GeV to PeV) for background\nevaluation in the search for astrophysical neutrinos at neutrino telescopes. Moreover, the muon\ncharge ratio in the TeV range allows to constrain the kaon production in a phase space inac-\ncessible to collider and fixed target experiments, so it represents an important benchmark for\nneutrino flux calculations and the predicted νµ/ν̄µ ratio.\n\nIn the following Secs. the key features of the atmospheric lepton flux parameterizations and\ncharge ratio are recalled, then experimental results on the muon charge ratio in the TeV energy\nrange are reviewed.\n\n2 Inclusive production of atmospheric muons and neutri-\nnos\n\nThe atmospheric leptons detected at the Earth’s surface and underground are produced in the\nforward fragmentation region, with a large Feynman x = Emeson/Enucleon ≈ Elepton/Enucleon.\nAssuming scaling of particle production in the forward region, the inclusive fluxes of muons and\nmuon neutrinos are well described by [1]\n\nΦl(El) =\nΦN (El)\n\n1− ZNN\n\nNpar∑\n\ni=1\n\nail ZNi\n1 + bilEl cos θ∗/εi\n\nwhere ΦN (El) ' Φ0E\n−(γ+1)\nl is the spectrum of primary nucleons (evaluated at the lepton energy\n\nin the atmosphere El) with spectral index γ + 1 ' 2.7. The sum is over the contributions of\nlepton parents, i.e. charged pions, kaons, charmed particles, etc. The constants ail and bil\n\n2 Magellan Workshop 2016\n\nNICOLETTA MAURI\n\n48 Magellan Workshop 2016\n\n\n\ncontain the kinematic factors for the i-th parent decay into lepton l. The critical energy\nεi/ cos θ∗ is defined as the energy for meson i above which interaction processes dominate over\ndecay, and θ∗ is the zenith angle at the lepton production point.\n\nThe spectrum weighted moments for secondary particle production are defined as:\n\nZij =\n1\n\nσij\n\n∫ 1\n\n0\n\ndσij(x)\n\ndx\nxγdx\n\nwhere σij is the inclusive cross-section for the production of a particle j from the collision of\na particle i with a nucleus in the atmosphere and x = Ej/Ei is the energy fraction carried by\nthe secondary particle. In the scaling approximation the particle production depends only on\nx and not explicitly on the energy. The approximation is valid since it holds in the forward\nfragmentation region, which is enhanced by the xγ weighting. Using the Z factors makes explicit\nthat the particle production in cosmic ray cascades is concentrated in the forward fragmentation\nregion at large x.\n\nFor each i-th contribution to the muon flux, at energies below the critical energy εi most\nmesons decay and the lepton flux follows the same power law of primary cosmic rays. For\nEµ � εi(θ), the meson interations dominate, so the energy spectrum steepens by one power\nand the relative contribution is suppressed. Therefore each contribution to the muon charge\nratio produced by different muon parents can be disentangled by studying the muon charge\nratio as a function of the “vertical surface energy” Eµ cos θ∗.\n\nConsidering only the conventional π and K parent mesons, the inclusive muon flux separated\nfor µ+ and µ− is given by\n\nΦµ±(Eµ) ∝\n(\n\naπZNπ±\n\n1 + bπµEµ cos θ∗/επ\n+\n\naKZNK±\n\n1 + bKµEµ cos θ∗/εK\n\n)\n\nThe increasing kaon contribution to the muon flux in the range between 100 GeV and the TeV,\nεπ < Eµ cos θ∗ < εK , is therefore explained by the two energy scales which determine the pion\nand kaon contributions, επ ' 115 GeV and εK ' 850 GeV.\n\nThese energies have to be compared with the corresponding value for charmed particles,\nεX > 107 GeV. The prompt muon component is therefore isotropically distributed since the\ncorresponding cos θ∗ factor is suppressed, at least in the TeV region.\n\n2.1 Parameterization of the muon charge ratio\n\nThe atmospheric muon charge ratio is larger than unity because the primary cosmic rays are\nmostly protons, favoring the production of π+ and K+ over π− and K− in the forward region.\nEach meson is likely to have an energy close to the one of the primary nucleon (high xF ) and\ncomes from its fragmentation (valence quarks, majority of u), therefore the positive charge\nexcess is preserved.\n\nConsidering the pion component, the isospin symmetry Zpπ+ = Znπ− (i.e. the inclusive π+\n\nspectrum from protons is equal to the π− spectrum from neutrons) allows expressing the pion\ncontribution to the muon charge ratio in terms of the fraction of positive pions fπ+ , depending\nonly on Z factors and the primary proton excess δ0 = (p− n)/(p+ n) [3]. Assuming Feynman\nscaling, the charge ratio does not depend on the energy Eµ nor Eπ. The constant value of\nthe muon charge ratio measured over the 10–100 GeV energy range, where almost only pion\n\nMagellan Workshop 2016 3\n\nTHE ATMOSPHERIC MUON CHARGE RATIO: A PROBE TO CONSTRAIN THE . . .\n\nMagellan Workshop 2016 49\n\n\n\ncontribute, is an experimental evidence of the validity of the Feynman scaling in the forward\nregion, see Sec. 3.\n\nThe kaon component does not have the same isospin symmetry between positive and nega-\ntive kaons. The associated production N + air→ ΛK+(+ anything), which has no analog for\nK−, largely favors K+ over K− (ZpK+ � ZnK−), therefore the K+/K− ratio is larger than the\nπ+/π− ratio [1]. Hence the muon charge ratio increases with the increasing kaon contribution.\nAn analytical description of the contributions in terms of δ0, fπ+ and kaon Z factors is given in\nRef. [3], and used by the OPERA Collaboration to extract the ZpK+ factor (see Sec. 3.1). The\nassociated production process and ZpK+ in particular cannot be constrained by accelerator ex-\nperiments (high pseudo-rapidity regions), and the Monte Carlo predictions by various hadronic\ninteraction generators differ up to several factors. This has a strong impact on the evaluation\nof the flux of TeV atmospheric neutrinos, which are dominated by kaon production.\n\nSummarizing, in order to disentangle the different parent components to the muon flux\n(π,K, charm) the correct variable to describe the evolution of Rµ is the vertical surface energy\nEµ cos θ∗ [4], as seen in Sec. 2. This approach assumes a constant primary composition. If\nthe energy dependence of the proton excess δ0 in the primary cosmic rays is considered, the\nparameterization described in Ref. [3] should be used. Since δ0 depends on the primary nu-\ncleon energy, which has an almost linear relation with the muon energy (EN ' 10Eµ for TeV\nmuons [3]), the dependency on the surface muon energy Eµ and the zenith projection cos θ\nshould be considered separately, see Sec. 3.1.\n\n3 Measurements of the atmospheric muon charge ratio\n\nIn the pion dominated energy region (Eµ . 100 GeV), the muon charge ratio Rµ has been mea-\nsured by several experiments on surface, by balloon-borne detectors and at shallow depths [5].\nIts value is constant, Rµ ' 1.27, as expected assuming the validity of Feynman scaling in the\nfragmentation region [1].\n\nThe increasing kaon contribution from a few hundred GeV to a few TeV causes a smooth\ntransition to a higher value of the muon charge ratio, Rµ ' 1.38. Recent results were presented\nby the CMS [6], MINOS [7, 8] and OPERA [9, 10] Collaborations in this energy range, around\nvertical surface energy Eµ cos θ∗ ∼ 1 TeV. Underground experiments naturally select high\nenergy down-going muons and the minimum energy threshold is fixed by the rock overburden\nsurrounding the detector. The evaluation of the muon surface energy Eµ depends on the rock\ndepth crossed by the muon coming from a particular direction and therefore the distribution of\nEµ cos θ∗ is related to the shape of the overburden.\n\nKey elements for the accurate determination of the muon charge and momentum in the\nO(TeV) energy range are magnetized detectors and large depth. MINOS and OPERA are long-\nbaseline neutrino experiments located deep underground, while CMS is a collider experiment\nlocated at shallow depth but provided with an intense magnetic field. In the following Section\nthe results from the OPERA experiment will be reviewed.\n\n3.1 OPERA results\n\nThe OPERA experiment is a hybrid electronic detector/emulsion apparatus exposed to the\nCNGS neutrino beam from 2008 up to 2012. It is located in the underground Gran Sasso\nlaboratory, at an average depth of 1400 m of rock corresponding to 3800 m.w.e. OPERA is\n\n4 Magellan Workshop 2016\n\nNICOLETTA MAURI\n\n50 Magellan Workshop 2016\n\n\n\nthe deepest experiment able to measure charge-separated atmospheric muons. The minimum\nsurface muon energy threshold is ∼1 TeV (1.4 TeV averaged over all the directions and rock\ndepths). The detector is composed of two identical parts, called supermodules, each consist-\ning of a target section and a magnetic spectrometer. The target consists of scintillator strips\ninterleaved with Emulsion Cloud Chambers, the spectrometer is a large dipole iron magnet\ninstrumented with Resistive Plate Chambers. The magnetic field is almost uniform, with inten-\nsity 1.53 T, and directed along the vertical axis with opposite orientations in the two magnet\narms. A muon crossing the spectrometer is deflected in the horizontal plane. The deflection is\nmeasured by six stations of vertical drift tubes, the Precision Trackers (PT), grouped in 3 pairs\nplaced upstream of the first arm, in between the two arms and downstream of the second arm.\nThe charge and momentum reconstruction is performed for tracks crossing at least one magnet\narm using the angle ∆φ in the bending plane, i.e. the difference between the track directions\nreconstructed by two PT stations before and after each magnet arm. For nearly horizontal\nmuons up to four bending angles can be measured in the two dipole magnets.\n\nCosmic ray induced events in OPERA are reconstruted with a dedicated software procedure\neffective at identifying single and multiple muon tracks (muon bundles). Due to the charge-\nsymmetry of the dipole magnet, the acceptance for µ+ and µ− is the same and, depending on\nthe track topology, the maximum detectable momentum varies from a few hundred GeV/c up\nto 1 TeV/c [11, 10]. Data were collected with both magnetic field polarities in order to minimize\nsystematic errors due to misalignment of PT stations.\n\nOPERA reported a first measurement of the atmospheric muon charge ratio using the 2008\nRun data [9] and the final results using the complete statistics from 2008 up to 2012 [10]. In the\nlatter analysis the two data sets collected with opposite magnet polarities were combined reach-\ning the most accurate measurement to date of Rµ in the TeV energy region. The muon charge\nratio was computed separately for single muons, Rµ(nµ = 1) = 1.377±0.006 (stat.)+0.007\n\n−0.001 (syst.),\n\nand for muon bundles, Rµ(nµ > 1) = 1.098± 0.023 (stat.)+0.015\n−0.013 (syst.). This is the first obser-\n\nvation of a decrease in the charge ratio of high multiplicity events with respect to single muon\nevents. The dilution effect in Rµ is expected since the multiple muon sample selects events gen-\nerated by heavier primary cosmic rays and secondaries with a low value of Feynman-xF , coming\nfrom the central region [11]. Recently also the MINOS Collaboration provided a measurement\nof the multiple-muon charge ratio, Rµ(nµ > 1) = 1.104 ± 0.006 (stat.)+0.009\n\n−0.010 (syst.) [12], in\nagreement with the OPERA result.\n\nThanks to the Gran Sasso orography, the amount of rock crossed by muons is not directly\nrelated to the zenith angle, and the high energy tail is not completely suppressed by the cos θ\nfactor. OPERA presented results on Rµ (for single muons) as a function of the vertical surface\nenergy Eµ cos θ∗, fitting data to the parameterized model described in Ref. [4]. The atmospheric\nmuon charge ratio was measured in a large Eµ cos θ∗ range, from 500 GeV up to ∼ 10 TeV,\nand plotted in Fig. 2. With an average value 〈Eµ cos θ∗〉 ' 2 TeV, OPERA is the magnetized\nexperiment measuring the charge ratio at the largest vertical surface energy. The fit of OPERA\nand L3+C data, shown in Fig. 2, yields the fractions of charged mesons decaying into positive\nmuons fπ+ = 0.5512 ± 0.0014 and fK+ = 0.705 ± 0.014. The prompt muon component does\nnot significantly contribute to Rµ up to Eµ cos θ∗ . 10 TeV.\n\nTaking into account the possible variation of the primary cosmic ray composition requires\nto disentangle the energy and zenith angle dependencies [3], as seen in Sec. 2.1. The fit in two\ndimensions (Eµ, cos θ∗) yields the proton excess in primary cosmic rays δ0 = (p− n)/(p+ n) =\n0.61 ± 0.02 at primary energy 〈EN 〉 ≈ 20 TeV/nucleon, and the spectrum weighted moment\nrelated to the associated kaon production, ZpK+ = 0.0086 ± 0.0004. The ZpK+ factor, here\n\nMagellan Workshop 2016 5\n\nTHE ATMOSPHERIC MUON CHARGE RATIO: A PROBE TO CONSTRAIN THE . . .\n\nMagellan Workshop 2016 51\n\n\n\n* (GeV)θ cos µε\n210 310 410 510\n\n o\nn\n\n s\nu\n\nrf\nac\n\ne\nµ\n\nR\n\n1\n\n1.1\n\n1.2\n\n1.3\n\n1.4\n\n1.5\n\n1.6\nOPERA\n\nL3+C\n\nUtah\n\nMINOS\n\nCMS\n\nK modelπ\nK + RQPM modelπ\nK + QGSM modelπ\nK + VFGS modelπ\n\nFigure 2: The atmospheric muon charge ratio measured by OPERA (black points) as a function\nof Eµ cos θ∗. OPERA and L3+C data are fitted together and the fit result is shown by the\ncontinuous line [10]. Results from MINOS Near and Far Detectors [7, 8] and CMS [6] are also\nshown.\n\ndetermined for the first time, allows to predict the atmospheric νµ/ν̄µ ratio in the TeV range:\nthe expected ratio for muon neutrinos increases from νµ/ν̄µ ≈ 1.5 at low energy to ≈ 2.3 above\na TeV [13].\n\n4 Conclusions\n\nThe results on the atmospheric muon charge ratio from the OPERA, MINOS and CMS ex-\nperiments show an increase of Rµ as a function of the vertical surface energy Eµ cos θ∗ in the\nrange between hundreds GeV and a few TeV. The measurements of OPERA in the TeV energy\nregion are compatible with a simple parametric model where the rise is due to the increasing\nkaon contribution to the muon flux. No significant contribution from charm decay is observed\nup to energies ∼10 TeV. A future experimental measurement of Rµ in the region Eµ cos θ∗ > 10\nTeV, with a new detector at very large depths, could shed light on the prompt component even\nbefore the crossover between conventional and prompt muon fluxes.\n\nThe OPERA measurement of Rµ in the highest energy range to date allows to constrain the\nassociated kaon production ΛK+ in high energy primary interactions in the forward region. The\nresult attains to a phase space inaccessible to accelerator experiments (high xF ↔ high pseudo-\nrapidity). It determined for the first time the spectrum weighted moment ZpK+ , fundamental\nto accurately predict the atmospheric νµ/ν̄µ ratio [13]. The ZpK+ factor was extracted together\nwith the primary proton excess δ0, which is consistent with direct measurements of the primary\n\n6 Magellan Workshop 2016\n\nNICOLETTA MAURI\n\n52 Magellan Workshop 2016\n\n\n\ncomposition [3]. The energy behaviour of Rµ measured by OPERA above a few TeV, compatible\nwith the plateau corresponding to the asymptotic kaon contribution, supports the validity of\nFeynman scaling in the fragmentation region up to primary energies/nucleon around 200 TeV.\n\nReferences\n[1] T.K. Gaisser, Cosmic Rays and Particle Physics (Cambridge University Press, Cambridge, 1990)\n\n[2] A. Fedynitch, LHC-CR Workshop: Results and prospects of forward physics at the LHC (2013)\n\n[3] T.K. Gaisser, Astropart. Phys. 35, 801 (2012)\n\n[4] P.A. Schreiner, J. Reichenbacher, M.C. Goodman, Astropart. Phys. 32 (1), 61 (2009)\n\n[5] T. Hebbeker and C. Timmermans, Astropart. Phys. 18, 107 (2002)\n\n[6] CMS Collaboration, Phys. Lett. B 692, 83 (2010)\n\n[7] P. Adamson et al. (MINOS Collaboration), Phys. Rev. D 76, 052003 (2007)\n\n[8] P. Adamson et al. (MINOS Collaboration), Phys. Rev. D 83, 032011 (2011)\n\n[9] N. Agafonova et al. (OPERA Collaboration), Eur. Phys. J. C 67, 25 (2010)\n\n[10] N. Agafonova et al. (OPERA Collaboration), Eur. Phys. J. C 74, 2933 (2014)\n\n[11] N. Mauri, Ph.D. Thesis, Università di Bologna (2011)\nhttp://inspirehep.net/record/1393857/files/phd thesis-BO-2011 05 20-mauri.pdf\n\n[12] P. Adamson et al. (MINOS Collaboration), Phys. Rev. D 93, 052017 (2016)\n\n[13] T.K. Gaisser, EPJ Web of Conf. 99, 05002 (2015)\n\nMagellan Workshop 2016 7\n\nTHE ATMOSPHERIC MUON CHARGE RATIO: A PROBE TO CONSTRAIN THE . . .\n\nMagellan Workshop 2016 53\n\n\n\n\n\nA Search for Lorentz and CPT Violation Using\n\nthe T2K Near Detectors\n\nGary Alexander Clifton1\n\n1Colorado State University, Fort Collins, Colorado USA\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/36\n\nLorentz symmetry violation (LV) arises when the behavior of a particle depends on its\ndirection or boost velocity. The Standard Model Extension (SME) is a general theoreti-\ncal framework that includes both General Relativity and the Standard Model while also\nallowing for the breaking of particle Lorentz symmetry through a set of controlling coef-\nficients. A search for LV at the T2K near detectors with baselines of 280 m is described.\nThe protons-on-target normalized neutrino event rate at the T2K near detectors is used to\nsearch for LV using a shape-only analysis via Fast Fourier Transform (FFT) analysis and\na binned log-likelihood fit. No indication of LV is observed with either method in the T2K\nnear detector data.\n\n1 Conventional Neutrino Oscillations\n\nFor non-interacting neutrinos, the Hamiltonian eigenstates are the mass eigenstates and the\nmixing matrix is then equated to the Pontecorvo, Maki, Nakagawa, and Sakata (PMNS) ma-\ntrix. The PMNS matrix may be parameterized with three mixing angles (θ12, θ23, θ13) and one\ncomplex phase factor (δCP ). The probability of neutrino flavor oscillation in natural units is\n\nPνα→νβ (L) =δαβ − 4\n∑\n\ni>j\n\nRe(U∗\nαiUβiUαjU\n\n∗\nβj) sin\n\n2(\n∆m2\n\nij\n\n4E\nL) (1)\n\n+2\n∑\n\ni>j\n\nIm(U∗\nαiUβiUαjU\n\n∗\nβj) sin(\n\n∆m2\nij\n\n2E\nL)\n\nwhere\n∆m2\n\nij = m2\ni −m2\n\nj (2)\n\nis the mass-squared splitting, Uγk are the elements of the PMNS matrix, L is the baseline of the\nneutrino experiment, and E is the neutrino energy. Thus, conventionally, neutrino oscillations\nare driven by a unique neutrino mass.\n\n2 T2K Experiment\n\nT2K utilizes a 2.5◦ off-axis neutrino beam for optimal detection of νe appearance at a peak\nneutrino energy of 0.6 GeV with a baseline of 295km at the Super Kamiokande far detector.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 55\n\n\n\nThe experiment consists of a νµ beam produced at the JPARC facility in Tokai, Japan, near\ndetector site, and a far detector site. An on-axis near detector, called the interactive neutrino\ngrid (INGRID), monitors the beam stability and intensity while the off-axis near detector, called\nthe near detector at 280m (ND280), is used to study neutrino interactions for different nuclear\ntargets as well as study neutrino kinematics. Both near detectors are located at 280m from the\ngraphite target used to create the T2K neutrino beam.\n\n2.1 INGRID\n\nINGRID was designed to measure the beam intensity and direction by utilizing neutrino inter-\nactions on iron [1] and lies on axis at 280m from the graphite target [2]. INGRID is arranged\nin a cross configuration with 14 identical modules [4] to detect neutrino interactions from the\nbeam. In addition to these 14 modules, two shoulder modules exist to check the axial symmetry\nof the neutrino beam. Each module consists of alternating layers of scintillator and iron plates.\nIn each module, there are 11 scintillator tracking planes that consist of 24 horizontal and 24\nvertical scintillator bars glued together.\n\nFigure 1: A diagram showing the layout of an INGRID module.\n\n2.2 ND280\n\nND280 was designed to perform several measurements [2]:\n\n1. Measure the neutrino flux and energy from the beam\n\n2. Measure the inherent νe contamination of the beam\n\n3. Measure neutrino event rates\n\nIt is a composite detector encased in the UA1 dipole magnet. The π0 detector, or P0D, [7] was\nconstructed to measure the following neutral current interaction on a water target:\n\nνµ +N → νµ +N + π0 +X (3)\n\nDownstream of the P0D are three time projection chambers (TPCs). The TPCs measure the\ncharge, momentum, and particle types of the different charged particles that are produced in\nneutrino interactions below a few GeV [2]. Each TPC consists of an inner and outer box [9]. The\ninner box contains an argon gas mixture for charged particles to ionize during their interactions.\n\n2 Magellan Workshop 2016\n\nGARY ALEXANDER CLIFTON\n\n56 Magellan Workshop 2016\n\n\n\nFigure 2: A blowout diagram of ND280 showing the different sub-detectors.\n\nThe outer box contains a CO2 atmosphere which provides electrical insulation between the inner\nbox and ground while also keeping atmospheric oxygen from entering the inner box. In addition\nto the P0D, two Fine Grained Detectors (FGD) were installed to provide a target for studying\nneutrino interactions as well as tracking of charged particles [8]. Each FGD consists of finely\nsegmented scintillator bars arranged in X and Y planes. The electromagnetic calorimeters\n(ECals) of ND280 are sampling calorimeters located inside the magnet yokes and enclose the\nvarious subdetectors of ND280 (P0D, TPCs, FGDs). The ECals serve to measure the energy\nand direction of charged particles that exit the sides of the ND280 subdetectors in order to\nidentify particle types [5]. Each ECal module consists of alternating layers of scintillator bars\nand lead sheets. The Side Muon Range Detector (SMRD) system is located between the air\ngaps in each magnet yoke and uses scintillator planes to detect high angle muons that escape\nND280 with respect to the beam line and measure their momentum [6].\n\n3 The Standard Model Extension\n\nThe Standard Model Extension (SME) is a general theoretical framework conceived to facilitate\nexperimental investigations of LV and CPT violation (CPTV) [3]. It is an effective field theory\nthat contains General Relativity, the Standard Model, and all possible Lorentz violating oper-\nators. The SME allows for the breaking of Particle Lorentz symmetry which produces a tensor\nbackground field that permeates throughout the universe. Neutrinos couple to this background\nfield with some given strength and the physics that the SME predicts becomes dependent, for\nexample, on the absolute direction of neutrino propagation. This dependency can produce a\nnumber of effects, namely sidereal variations. LV is controlled by a set of coefficients which\nexperiments may measure while working in the sun centered inertial frame. The probability of\noscillation in the SME at short baselines is given by\n\nMagellan Workshop 2016 3\n\nA SEARCH FOR LORENTZ AND CPT VIOLATION USING THE T2K NEAR DETECTORS\n\nMagellan Workshop 2016 57\n\n\n\nP (νµ → νx) = L2|Cµx + (As)µx sin(ω⊕T⊕) + (Ac)µx cos(ω⊕T⊕)\n\n+ (Bs)µx sin(2ω⊕T⊕) + (Bc)µx cos(2ω⊕T⊕)|2 (4)\n\nwhere ω⊕ is the Earth’s sidereal frequency and T⊕ is the sidereal time of the neutrino event\nin the detector. The various amplitudes associated with each sidereal time harmonic consist of\nthe SME coefficients (aL)\n\nα\nµx and (cL)\n\nαβ\nµx , the neutrino energy E, and geographical information\n\nabout how the neutrino beam is setup on Earth. Thus, in the SME, neutrino oscillations vary\nwith sidereal time producing a sidereal variation in the neutrino event rate at a given detector.\n\n4 LV Analysis\n\nTo perform a LV search at the T2K near detectors, T2K data taken from 2010 to 2013 is used.\nThis data was taken in various T2K data run periods throughout this time. For INGRID, all\nthe data is combined into a single data sample, while for ND280 since the P0D can be configured\nwith or without water, two data samples are used corresponding to each configuration of the\nP0D. The POT normalized neutrino event rate as a function of Local Sidereal Phase (LSP) for\neach data set is shown in Figures 3.\n\nLSP = mod\n( T⊕\n23h56m4.0916s\n\n)\n(5)\n\nFigure 3: Corrected νµ event rate versus LSP in INGRID (top left), ND280 water-in (top right),\nand ND280 water-out (bottom).\n\nEach data set consists of 32 LSP bins to avoid aliasing effects in the Fast Fourier Transform\nmethod (described below). Each data sample was analyzed for various sources of systematic\n\n4 Magellan Workshop 2016\n\nGARY ALEXANDER CLIFTON\n\n58 Magellan Workshop 2016\n\n\n\nuncertainty that could mimic a LV signal. All sources of systematic error were found to be\nnegligible compared to statistical uncertainty and corrections were calculated for each source\nand applied to the data.\n\nThe analysis is broken up into two parts: a Fast Fourier Transform (FFT) method and a binned\nlikelihood fit method. The FFT method is applied to both INGRID and ND280 data while\nonly the INGRID data is used to perform the binned likelihood fit as it is a significantly higher\nstatistical sample.\n\n4.1 Fast Fourier Transform Method\n\nA set of 10,000 data-driven toy experiments is created for which any potential LV signal is\nremoved by randomizing the various LSP bins for a given sample based off the total number of\nbeam spills in that sample. An example is shown in Figure 4.\n\nFigure 4: Examples of a ND280 flat toy MC for the water-in and water-out samples (left,\nmiddle) and an INGRID flat toy MC (right).\n\nFor a given detector, each toy is passed to a FFT tool which calculates the magnitude of the\nFFT. As such, a distribution of magnitude values for a given Fourier mode can be created for\neach detector. Such a distribution is shown in Figure 5. A 99.7% confidence level hypothesis\ntest is performed to determine the significance of the FFT values in data. An overall detection\nthreshold is calculated from the Fourier mode distributions of each Fourier mode as the largest\ndetection threshold. A p-value is calculated utilizing the value observed in data, the Fourier\nmode distributions, and the total number of toy MC.\n\nThe corrected INGRID, ND280 water-in, an ND280 water-out νµ event rate as a function of\nLSP was analyzed with the method described above. The results of the FFT analysis can be\n\nMagellan Workshop 2016 5\n\nA SEARCH FOR LORENTZ AND CPT VIOLATION USING THE T2K NEAR DETECTORS\n\nMagellan Workshop 2016 59\n\n\n\nseen in Figure 6, Table 1, Table 2, and Table 3. The Fourier modes of interest to this analysis\nare below the 3σ detection threshold.\n\nFigure 5: Distribution of the magnitude of the first Fourier mode for the 10,000 flat toy exper-\niments of ND280 water-out. The distribution is fitted with a Rayleigh function.\n\nFourier Mode[n]\n0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5\n\nM\nag\n\nF\nF\n\nT\n\n2−10\n\n1−10\n\n1\n\n10\n\nFigure 6: Magnitude of each Fourier mode in case of INGRID data (left), ND280 water-in (mid-\ndle) data, and water-out (right) data after all corrections. The red horizontal line corresponds\nto the overall 3σ detection threshold.\n\n6 Magellan Workshop 2016\n\nGARY ALEXANDER CLIFTON\n\n60 Magellan Workshop 2016\n\n\n\nFourier Mode Threshold Magnitude p-value\n1 0.026 0.01076 0.35\n2 0.026 0.00930 0.48\n3 0.026 0.00620 0.69\n4 0.026 0.00893 0.51\n\nTable 1: FFT results for INGRID.\n\nFourier Mode Threshold Magnitude p-value\n1 0.4765 0.33537 0.055\n2 0.4765 0.28221 0.126\n3 0.4765 0.46355 0.004\n4 0.4765 0.15999 0.511\n\nTable 2: FFT results for ND280 water-in.\n\nFourier Mode Threshold Magnitude p-value\n1 0.4947 0.10501 0.765\n2 0.4947 0.26948 0.162\n3 0.4947 0.12856 0.659\n4 0.4947 0.10649 0.751\n\nTable 3: FFT results for ND280 water-out.\n\nWhile the third Fourier mode p-value in the water-in sample is larger than the significance\n(0.003), it is still very close to it. If this were truly a LV effect in this data sample, this effect\nis expected to appear in the other samples. Since it does not, this affect is attributed to a\nstatistical fluctuation. Thus, it is concluded that the INGRID, ND280 water-in, and ND280\nwater-out data are consistent with no sidereal variation in the relevant Fourier modes using the\nFFT analysis method. 99.7%C.L. upper limits on the SME coefficients may be extracted for\nthe each sample utilizing the following procedure:\n\n1. Set all but one SME coefficient to zero\n\n2. Increase the size of the SME coefficient in a set of 10,000 signal toy Monte-Carlo until the\nnominal threshold has been crossed\n\n3. Take the corresponding value of the coefficient as the upper limit\n\nThe 99.7%C.L. upper limits of the SME coefficients in each sample using this procedure are\nshown in Table 4.\n\nMagellan Workshop 2016 7\n\nA SEARCH FOR LORENTZ AND CPT VIOLATION USING THE T2K NEAR DETECTORS\n\nMagellan Workshop 2016 61\n\n\n\nCoefficient ND280 Water-in ND280 Water-out INGRID\naTL - - -\naXL 15.7 GeV 15.4GeV 4.8 GeV\naYL 16.3 GeV 17.4GeV 4.8 GeV\naZL - - -\ncTT\nL - - -\ncTX\nL 13.9 10.8 0.9\ncTY\nL 13.9 13.0 0.9\ncTZ\nL - - -\n\ncXX\nL 54.0 57.0 3.8\ncXY\nL 25.0 25.0 1.6\ncXZ\nL - - 3.1\ncY Y\nL 54.0 57.0 3.8\ncY Z\nL - - 3.1\ncZZ\nL - - -\n\nTable 4: Standard Model Extension 3σ upper limits on SME coefficients related to νµ → νe\noscillation, for ND280 water-in and water-out, and INGRID (all values given in ×10−20).\n\n4.2 Binned Likelihood Method\n\nDue to the large degree of correlation, a five coefficient binned likelihood fit was developed in-\nstead of a full fourteen-coefficient to extract limits on the SME coefficients. The five coefficients\nare the amplitudes associated with each sidereal time harmonic in Equation 4. A binning of 32\nbins in LSP was chosen for the likelihood fit, just as in the FFT method. For each LSP bin,\nthe statistical errors are assumed to be Gaussian and the likelihood may be constructed as:\n\nL =\n\n32∏\n\ni=1\n\ne\n− (di−mi)\n\n2\n\n2σ2\ni (6)\n\nwhere, di and mi are the POT normalized number of events in the ith LSP bin for data and\ntoy MC respectively, and\n\nσi =\nNνµ\n\nNPOT\n\n√\nδN2\n\nνµ\n\nN2\nνµ\n\n+\nδN2\n\nPOT\n\nN2\nPOT\n\n, (7)\n\nwith Nνµ and NPOT being, respectively, the number of neutrino events and the number of\nprotons on target in LSP bin i, with δNνµ and δNPOT the corresponding statistical errors.\nThe fitter was run over the corrected INGRID data. Table 5 shows the best fit values for each\ncoefficient.\n\n(C)ab (As)ab (Ac)ab (Bs)ab (Bc)ab\nBest fit (10−20 GeV) −0.61+2.63\n\n1.41 0.38+1.83\n−2.60 −1.55+4.55\n\n−1.44 0.06+0.87\n−1.00 0.38+0.80\n\n−1.57\n\nTable 5: Best fit values with 1σ errors, and 2σ upper limit values on the different Standard\nModel Extension coefficients using the likelihood method.\n\nAll limits set by INGRID are consistent with zero (i.e. no LV) to 1σ.\n\n8 Magellan Workshop 2016\n\nGARY ALEXANDER CLIFTON\n\n62 Magellan Workshop 2016\n\n\n\n5 Summary\n\nThe near detectors of T2K were used to search for indications of LV in the neutrino data.\nThe two methods utilized (FFT method and binned likelihood fit) show that all data sets used\nare consistent with the null hypothesis, namely, no sidereal variations. Figure 7 compares the\nresults using the FFT method of INGRID and ND280 with MiniBooNE and MINOS (left) and\nthe results of the likelihood method for INGRID and MiniBooNE (right). For the likelihood,\nall limits set by INGRID are consistent with no LV to 1σ. (Bs)ab and (Bc)ab have now been\nconstrained in INGRID as they previously were not.\n\nFigure 7: Comparison of SME Coefficients associated with νe appearance INGRID and ND280\nwith other experiments using the FFT method (left) and comparison between the T2K (blue)\nand MiniBooNE (red) best fit values and 1σ limits (right).\n\nIt should be stressed that this level of precision is still very valuable to theorists. T2K probes\na different direction in the Sun-centered reference frame, and thus samples a different portion\nof the coefficient space. This information helps theorist improve their theories so that experi-\nmentalists may continue to test them.\n\n6 Aknowledgements\n\nI thank the J-PARC team for the superb accelerator performance and CERN NA61 colleagues\nfor providing particle production data. I acknowledge the support of the US Department of\nEnergy for my research. Finally, I want to thank the organizers of the Magellan Workshop for\ngiving me the opportunity to present.\n\nReferences\n[1] T. Le et al. Overview of the T2K long baseline neutrino oscillation experiment, Proceedings of the DPF-2009\n\nConference (2009).\n\n[2] K. Abe et al. Nuclear Instruments and Methods in Physics 69 106 (2011).\n\n[3] D. Colladay, A. Kostelecky Phys. Rev. D. 58 116002 (1998).\n\n[4] K. Abe et al. Nuclear Instruments and Methods in Physics. ”Measurements of the T2K neutrino beam\nproperties using the INGRID on-axis near detector” A694 (2012).\n\nMagellan Workshop 2016 9\n\nA SEARCH FOR LORENTZ AND CPT VIOLATION USING THE T2K NEAR DETECTORS\n\nMagellan Workshop 2016 63\n\n\n\n[5] D Allan et al. JINST. ”The Electromagnetic Calorimeter for the T2K Near Detector ND280” 8 (2013).\n\n[6] S. Aoki et al. Nuclear Instruments and Methods in Physics. ”The T2K Side Muon Range Detector” A698\n(2013).\n\n[7] S. Assylbekov et al Nuclear Instruments and Methods in Physics. ”The T2K ND280 Off-Axis Pi-Zero\nDetector” A686 (2012).\n\n[8] P.-A. Amaudruz et al Nuclear Instruments and Methods in Physics. ”The T2K Fine-Grained Detectors”\nA696 (2012).\n\n[9] N. Abgrall et al Nuclear Instruments and Methods in Physics. ”Time Projection Chambers for the T2K\nNear Detectors” A637 (2011).\n\n10 Magellan Workshop 2016\n\nGARY ALEXANDER CLIFTON\n\n64 Magellan Workshop 2016\n\n\n\nSearching for neutrino oscillation parameters in\n\nlong baseline experiments\n\nSampsa Vihonen\n\nUniversity of Jyvaskyla, Department of Physics, P.O. Box 35, FI-40014 University of Jyvaskyla,\nFinland\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/8\n\nDeveloping neutrino astronomy requires a good understanding of the neutrino oscillations\nmechanism. The European strategy for neutrino oscillation physics sets a high priority on\nfuture long baseline neutrino experiments with the aim to measure the intrinsic parameters\nthat govern the neutrino oscillations. In this talk we take a look at the next generation of\nlong baseline experiments and discuss their prospects in future research.\n\n1 Introduction\n\nThe dream of using neutrino telescopes as messengers of the universe in the future is built on the\nidea that the neutrino oscillation mechanism is eventually resolved in the neutrino oscillation\nexperiments. The prospects of these experiments include both determining the parameters of\nthe standard three-neutrino paradigm and probing non-standard interactions (NSI) that may\nfollow from potential Beyond Standard Model theories.\n\nNeutrino oscillation experiments are typically categorized into solar, reactor, atmospheric\nand accelerator-based experiments. In this work we focus on long baseline neutrino experiments\nwhere an intense muon neutrino beam is directed to traverse long distances underground.\n\nIn the Standard Model of particle physics (SM) neutrino sector the relationship between\nthe three neutrino mass states (ν1, ν2, ν3) and the three flavour states (νe, νµ, ντ ) is usually\nexpressed as a 3 × 3 matrix known as the PMNS matrix after Bruno Pontecorvo, Ziro Maki,\nMasami Nakagawa and Shoichi Sakata. The mixing of neutrino states is the source to the\nphenomenon that is known as neutrino oscillations. The PMNS matrix can be written in terms\nof the three mixing angles and one phase in the form:\n\nU =\n\n\n\n\n1 0 0\n0 c23 s23\n0 −s23 c23\n\n\n\n\n\n\n\nc13 0 s13e\n−iδCP\n\n0 1 0\n−s13e\n\niδCP 0 c13\n\n\n\n\n\n\n\nc12 s12 0\n−s12 c12 0\n0 0 1\n\n\n\n\n×diag\n(\neiα1/2, eiα2/2, 1\n\n)\n(1)\n\nwhere sij = sin θij and cij = cos θij for i, j = 1, 2, 3, θij = [0, π/2] are the intrinsic mixing\nangles, δCP = [0, 2π] is the Dirac CP violation phase and α1, α2 are two Majorana CP violation\nphases.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 65\n\n\n\nThe transition probability between two neutrino flavour states νl → νl′ is calculated as:\n\nPll′ =\n∑\n\nj,k\n\nU∗\nljUl′jUlkU\n\n∗\nl′ke\n\n−iL∆m2\njk/2E , (2)\n\nwhere L is the baseline length of the experiment and E is the energy of the neutrino pro-\nduced. Here neutrino mass squared differences ∆m2\n\njk = m2\nj −m2\n\nk for j, k = 1, 2, 3 are calculated\nfrom the neutrino masses m1,m2 and m3.\n\nThe expression in Equation (2) can be further simplified into\n\nPll′ = δll′ −\n∑\n\nj,k;j>k\n\n[\n4 sin2 ∆kj ReW\n\njk\nll′ − 2 sin 2∆kj ImW jk\n\nll′\n\n]\n, (3)\n\nwhere W jk\nll′ = U∗\n\nljUl′jUlkU\n∗\nl′k and ∆kj = ∆m2\n\nkjL/4E. It is seen from expression (3) that the\noscillation probabilities can be tuned to desirable values by choosing L/E conveniently.\n\nIn this paper, we analyze the potential of the planned long baseline neutrino oscillation\nexperiment LBNO at two tasks. On the one hand we study its performance for resolving the\nintrinsic θ23 octant degeneracy and on the other hand we study its ability to probe the matter\nNSI effects. That is, we derive numerically the values of θ23 for which the octant degeneracy\ncan be resolved and also calculate the new upper bounds that LBNO could give to |εmαβ | where\nα, β = e, µ, τ .\n\n2 Oscillation parameters: A general outlook\n\nThe present values of the oscillation parameters are given in Table 1. The values of the reactor\nand solar mixing angles (θ13 and θ12 respectively) have been determined in previous neutrino\nexperiments with good precision, but the value of the atmospheric mixing angle θ23 has yet to\nbe determined accurately. The other unknown quantities are the sign of ∆m2\n\n31 and the amount\nof CP violation δCP in the leptonic sector.\n\nParameter Value\n\nθ12 31.8◦... 37.8◦\n\nθ13 7.7◦... 9.9◦\n\nθ23 38.8◦... 53.3◦\n\n∆m2\n21 (7.11 ... 8.18) · 10−5eV2\n\n|∆m2\n31| (2.20 ... 2.65) · 10−3eV2\n\nδCP 0 ... 360◦\n\nTable 1: Standard neutrino oscillation parameters [1]. For the unknown δCP we have denoted\nthe values considered in numerical calculations, and the extra phases α1,2 only come into play\nin double-beta decay experiments.\n\n2 Magellan Workshop 2016\n\nSAMPSA VIHONEN\n\n66 Magellan Workshop 2016\n\n\n\n3 Simulation of long baseline neutrino experiments\n\nIn LBNO the aim is to send neutrino and antineutrino beams, produced at the CERN SPS\naccelerator, towards the Pyhäsalmi mine, located in central Finland at the distance 2288 km\nfrom CERN, where they will be measured using a two-phase Liquid Argon Time Projection\nChamber (LArTPC) combined with a magnetized muon detector (MIND). In this experiment,\nthe size of the LArTPC detector is planned to have 20 kton fiducial mass. In this phase a 0.75\nMW conventional neutrino beam from the CERN SPS will be used. In this work we also study\nLBNO with other baseline lengths.\n\nThe analysis is done by using the GLoBES simulation software [2, 3]. GLoBES calculates\nthe oscillation probabilities and the corresponding neutrino rates for any given set of oscillation\nparameter values. The standard set of the software simulates the neutrino propagation from\nsource to detector and computes the standard matter interactions (SI) for the distance that\nneutrinos travel. A software extension then allows us to perform the same calculation but\nmay also include all matter-induced NSI effects in the propagation. The software computes χ2\n\ndistributions to compare different sets of oscillation parameter values, both with SI and NSI.\n\nWe will determine for both mass hierarchies the 1σ, 2σ and 3σ sensitivity limit of the angle\nθ23 that LBNO can achieve with 5+5 –years neutrino and antineutrino run, allowing the CP\nphase to vary in the range 180◦ to +180◦. We will also determine the new 90% confidence level\nupper bounds that LBNO could set to the absolute values of εmαβ parameters that describe the\nstrength of non-standard interactions between neutrinos and the matter.\n\n4 Determination of the θ23 octant\n\nIn long baseline experiments one is interested mainly in the oscillation channels νµ → νµ\n(disappearance channel) and νµ → νe (appearance channel). In leading order, whereby omitting\nterms proportional to the small quantities ∆m2\n\n21/∆m2\n31 and sin θ13, the θ23 dependence of the\n\noscillation probability Pµµ is of the form sin2 2θ23 sin\n2 ∆ where ∆ = ∆m2\n\n31L/4E. Since\n\nPµµ(90\n◦ − θ23) = Pµµ(90\n\n◦), (4)\n\nthe measurements of the channel νµ → νµ are not suitable for resolving the octant degen-\neracy. There are non-leading terms that are sensitive to the octant but they are typically too\nmuch suppressed to be useful.\n\nIn a future long baseline neutrino experiment this problem with θ23 octants is resolved by\nstudying the electron appearance from νµ → νe oscillations. The leading term of Pµe has a\ndependence on θ23 that is not octant degenerate, and hence the νµ → νe oscillations bring in\nthe primary component to the determination of θ23 octant.\n\nUsing the GLoBES software, we simulated the hypothetical experiment that was described\nin the LBNO design study (see [4] for more details).\n\nThe results are presented in Figure 1. In both panels the white regions in the plots are the\nareas for which the values of θ23, δCP can be established with a confidence level greater than 3σ.\nSo for all θ23, δCP data points in these areas, one can eliminate with a confidence level larger\nthan 3 the possibility for these parameters to lie in the other octant. Conversely the coloured\nregions illustrate the cases where no such distinction is possible with the indicated confidence\nlevel.\n\nMagellan Workshop 2016 3\n\nSEARCHING FOR NEUTRINO OSCILLATION PARAMETERS IN LONG BASELINE . . .\n\nMagellan Workshop 2016 67\n\n\n\nFigure 1: Determination ability of the θ23 octant in LBNO [4] in normal hierarchy mode.\n\nIn Figure 1 we have also marked the MINOS favoured θ23 values 40◦ and 50◦ by green lines.\nIt is seen that for this particular setup the right θ23 octant can be asserted in NH with at least\n3σ confidence level. As for IH this limit is reached for the lower octant, but for the higher\noctant it fails to be reached for δCP values between -100◦ and +100◦.\n\n5 Constraining non-standard interaction parameters\n\nIn the low-energy regime, NSI can be parametrized in terms of effective charged current like\n(CC) and neutral current like (NC) Lagrangians, given respectively by\n\nLCC\nNSI = −2\n\n√\n2GF ε\n\nff ′,C\nαβ (ναγ\n\nµPLℓβ)(fγ\nµPCf\n\n′),\n\nLNC\nNSI = −2\n\n√\n2GF ε\n\nf,C\nαβ (ναγ\n\nµPLνβ)(fγ\nµPCf).\n\n(5)\n\nHere f and f ′ label charged leptons or quarks (ℓi, ui, di, i = 1, 2, 3), GF = 1.166 ×\n10−5GeV−2 is the Fermi coupling constant, α, β refer to neutrino flavour (e, µ, τ), and C = L,R\nrefers to the chirality structure of the charged lepton interaction, PL and PR being the chiral\n\nprojection operators. The NSI parameters εff\n′,C\n\nαβ and εf,Cαβ are dimensionless numbers. It is\nassumed here that the effective non-standard interactions have V–A Lorentz structure, and\nfor the charged fermions we allow both left-handed (PC = PL) and right-handed (PC = PR)\ncouplings. The charged current Lagrangian LCC\n\nNSI is relevant for the NSI effects in the source\nand detector, since both in the creation and detection processes involve charged fermions. The\nneutral current Lagrangian LNC\n\nNSI in turn is relevant for the NSI matter effects. The effective\nlow-energy Lagrangians (5) are assumed to follow from some unspecified beyond-the-standard-\nmodel theory after integrating out heavy degrees of freedom.\n\n4 Magellan Workshop 2016\n\nSAMPSA VIHONEN\n\n68 Magellan Workshop 2016\n\n\n\nConcerning the propagation in matter, NSI could contribute to the coherent forward scat-\ntering of neutrinos in the Earths crust. The effective Hamiltonian describing the time evolution\nof a neutrino state would take the form\n\nH =\n1\n\n2Eν\n\n\nU\n\n\n\n\n0 0 0\n0 ∆m2\n\n21 0\n0 0 ∆m2\n\n31\n\n\nU † + V\n\n\n (6)\n\nwhere Eν is the energy neutrino, U is the ordinary mixing matrix and V is the effective matter\npotential which includes both the SM and NSI matter effects:\n\nV = A\n\n\n\n\n1 + εmee εmeµ εmeτ\nεm∗\neµ εmµµ εmµτ\nεm∗\neτ εm∗\n\nµτ εmττ\n\n\n . (7)\n\nWe study numerically how the future neutrino oscillation experiments would constrain vari-\nous NSI parameters. We will concentrate here on the future long baseline neutrino experiments,\nusing the LBNO setup with a high-intensity beam, a baseline of 2300 km and 20 kt double-\nphase liquid argon detector as our benchmark. For each δCP value, we have calculated the 90%\nconfidence level contour baseline range 100–5000 km and log10 |εmαβ | range from 3.0 to 0.5. In\nevery case, a 90% confidence level contour is found and the results merged in a contour band.\nThe bands in (L, εmαβ)–plane are plotted in Figure 2.\n\nFigure 2: Constraining capability of LBNO for the absolute values of the non-standard inter-\naction parameters εmeµ and εmeτ . See Reference [5] for more details.\n\nMagellan Workshop 2016 5\n\nSEARCHING FOR NEUTRINO OSCILLATION PARAMETERS IN LONG BASELINE . . .\n\nMagellan Workshop 2016 69\n\n\n\n6 Summary\n\nIn this work we have presented the measurement potential of a potential future long baseline\nneutrino experiment in the determination of the θ23 octant and constraining the |εmeµ| and\n|εmeτ | parameter spaces for non-standard neutrino interactions that arise from the neutrino\npropagation in matter. In both cases we have demonstrated the interference both of these tasks\nsuffer from the δCP parameter whose value is still unknown. We used the recent LBNO design\nstudy as our benchmark model and performed the numerical study with the GLoBES software.\n\nReferences\n[1] D. V. Forero, M. Tortola, and J. W. F. Valle. Neutrino oscillations refitted. Phys. Rev., D90(9):093006,\n\n2014.\n\n[2] Patrick Huber, M. Lindner, and W. Winter. Simulation of long-baseline neutrino oscillation experiments\nwith GLoBES (General Long Baseline Experiment Simulator). Comput. Phys. Commun., 167:195, 2005.\n\n[3] Patrick Huber, Joachim Kopp, Manfred Lindner, Mark Rolinec, and Walter Winter. New features in the sim-\nulation of neutrino oscillation experiments with GLoBES 3.0: General Long Baseline Experiment Simulator).\nComput. Phys. Commun., 177:432–438, 2007.\n\n[4] C. R. Das, Jukka Maalampi, Joo Pulido, and Sampsa Vihonen. Determination of the 23 octant in LBNO.\nJHEP, 02:048, 2015.\n\n[5] Katri Huitu, Timo J. Karkkainen, Jukka Maalampi, and Sampsa Vihonen. Constraining the nonstandard\ninteraction parameters in long baseline neutrino experiments. Phys. Rev., D93(5):053016, 2016.\n\n6 Magellan Workshop 2016\n\nSAMPSA VIHONEN\n\n70 Magellan Workshop 2016\n\n\n\nPlanck 2015 Cosmological Results\n\nDaniela Paoletti1,2\n\non behalf of the Planck Collaboration\n\n1INAF/IASF-Bologna, via Gobetti 101, I-40129, Bologna, Italy\n2INFN-Sezione di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/19\n\nThe Planck satellite has observed the full sky at nine frequencies between 30 GHz and 1\nTHz from 2009 to 2013. It represents the third generation of satellites dedicated to the ob-\nservation of the Cosmic Microwave Background radiation (CMB). The CMB anisotropies\nrepresent the picture of primordial cosmological perturbations that originated the present\nstructures of the Universe and Planck has performed their measurement with unprece-\ndented accuracy in both temperature and polarization. In 2015 the Planck full mission\ndata and results have been released to the public , here we will present some of the main\ncosmological results.\n\n1 Introduction\n\nThe Cosmic Microwave Background (CMB) radiation is one of the most important source of\ninformation on the Universe origin, history, present and future status. It is the relic radiation\nfrom the Big Bang and it encodes the status of cosmological perturbations before stars and\nstructures were formed. It was generated when the universe was cool enough to allow the\nrecombination of neutral hydrogen (during the phase called recombination), increasing the mean\nfree path of photons to lenghts of the order of the Universe size. The CMB represents the first\npicture of the Universe just 400000 years after the Big Bang and its photons were emitted from\nan hypothetical surface called the Last Scattering Surface (LSS). The CMB is almost perfectly\nisotropic in the sky with a blackbody spectrum at a temperature of T=2.72548 ± 0.00057 K\n[1], the small anisotropies of the order of ∆T/T ∼ 10−5 are none other that the mirror of\nprimordial cosmological perturbations. The CMB, thanks to the properties of the Compton\nscattering, is linearly polarized and polarization represents a crucial additional information for\ncosmology. The CMB polarization anisotropies are usually not analysed in terms of Q and U\nStokes parameters but via their combination E and B [2]. Different cosmological models have\ndifferent predictions for CMB anisotropies in temperature and polarization and therefore the\nCMB is crucial to investigate and constrain the standard cosmological model and its extensions.\nIn this paper we ill briefly review the main cosmological results by Planck 2015 [3, 4, 5, 6, 7, 8].\n\nMagellan Workshop 2016 1Magellan Workshop 2016 71\n\n\n\n2 Planck 2015\n\nThe Planck mission1 is the third generation of satellites dedicated to the observation of the\nsky in the microwaves. It is an ESA project and was launched on the 9th of May 2009 and\nhas observed the sky continuosly from the 12th of August 2009 to the 23rd of October 2013\nin 9 bands between 30 GHz and 1 THz, it has angular resolutions between 5 and 30 arcmin\nand a sensitivity of ∆T/TCMB ∼ 2 × 10−6 [9]. The satellite is composed by two instruments\nbased on two different detector technologies: the Low Frequency Instrument (LFI), composed\nby radiometers at 30, 44, 70 GHz that operated for about 48 months , and the High Frequency\nInstrument (HFI), composed by spider-web bolometers at 100, 143, 217, 353, 545 and 857 GHz\nthat operated for about 30 months. The first data, and associated scientific results, release\ntook place in 2013 [10] with the delivery of the first 15 months of temperature data. In 2015\nthere has been the second data and result release [3] with the full mission data in temperature\nand, for the first time, also in polarization. With respect to the 2013 release some changes have\nbeen made to the data, in particular, there has been a strong improvement in the calibration,\nbringing the agreement between Planck and WMAP to less than few tenths of percent, the\neffect of the feature at ` ∼ 1800 in 2013 data has been removed, plus there have been some\nchanges in the data analysis tools like the likelihood and the foreground modelling inside the\nlikelihood.\n\n2.1 Maps\n\nPlanck has produced full sky maps at all nine frequencies in temperature and polarization\n[11, 12] . But the microwave sky is not only the CMB signal but it is the composition of CMB\nand all the astrophysical signals like the contamination by our Galaxy and by unresolved point\nsources, which are called foregrounds. In order to separate the different components of the sky\nand extract the CMB in temperature and polarization Planck has used four different component\nseparation methods all giving very consistent CMB maps [4]. In Fig.1 we show the results of\none of the method (SMICA) in temperature and polarization 2.\n\n2.2 Power Spectra\n\nTo perform a statistical analyisis of the CMB anisotropies, they are expanded in spherical\nharmonics ∆T (~x, n̂, τ) =\n\n∑∞\nl=1\n\n∑l\nm=−l alm(~x, τ)Ym,l(n̂) . The angular power spectrum is then\n\ndefined as Cl = 1\n(2l+1)Σm〈a∗lmalm〉. If the primordial fluctuations were Gaussian, the angular\n\npower spectrum would provide a complete description of the CMB anisotropies, whereas in\ncase of non-Gaussianities, like are predicted in different cosmological models, higher statisti-\ncal moments like the bispectrum and the trispectum may be different from zero. In Fig.2 we\nshow the Planck angular power spectra in temperature and polarization. The spectra shown\nare the temperature TT autocorrelation, the E-mode autocorrelation EE, the temperature-\nE-mode cross-correlation TE. We show also the B-mode polarization which has been derived\n\n1Planck (http://www.esa.int/Planck) is a project of the European Space Agency (ESA) with instruments\nprovided by two scientific consortia funded by ESA member states and led by Principal Investigators from France\nand Italy, telescope reflectors provided through a collaboration between ESA and a scientific consortium led and\nfunded by Denmark, and additional contributions from NASA (USA).\n\n2Due to ongoing data analyisis at the time of the release the lowest multipoles ` < 30 of the polarization\nmap have been filtered out, for a recent update on the low-` polarization see [13]\n\n2 Magellan Workshop 2016\n\nDANIELA PAOLETTI\n\n72 Magellan Workshop 2016\n\n\n\nFigure 1: The component separated maps in temperature, left panel, and polarization, right\npanel.\n\nwith a joint analyisis of the Planck and BICEP-2/Keck data [14]. The B-mode polarization\nis particularly important since it is not produced by the standard density perturbations, i.e.\nscalar perturbations, apart from a late time contribution on small angular scale by the CMB\ngravitational lensing. Therefore large scale B-modes in the standard cosmological model are\nrepresentative of tensor primordial perturbations and are considered the smoking gun of in-\nflation, in fact their amplitude would be directly connected to the enegy scale of inflation 3.\n\n2.3 Lensing\n\nCMB photons travel almost unperturbed from the LSS, but during their crossing of the large\nscale structure they are subjected to the gravitational effects of the matter distribution in the\nUniverse, the CMB lensing. The CMB lensing has distinctive signatures on the angular power\nspectra and it has a non-Gaussian contribution to the CMB anisotropies. The effect on the\nangular power spectra is a smoothing of the peaks in the temperature anisotropies, but most\nimportant, the lensing of the E-mode polarization generetes a non-zero B-mode polarization\nwith a peculiar shape which peaks on small angular scales. This signal is crucial since it\nrepresents one of the main contaminants for the detection of the primordial B-mode polarization\nfrom inflation. Using the non-Gaussian contribution of the lensing to the CMB anisotropies\nPlanck detected the lensing with a significance of more than 40 sigma and was able to extract the\ninformation on the lensing potential with unprecedented precision at the level of 2.5% accuracy\n[15]. It was also possible to reconstruct a full-sky map of the projected mass distribution. In\nFig.3 we show the results of both the analyisis.\n\n3 Cosmological Parameters\n\nThe Planck data are used to derive the constraints on the standard cosmological models and its\nextensions, exploring the cosmological parameter space using a a Markov Chain Monte Carlo\napproach, e.g. with the public code CosmoMC [16]. To derive the constraints presented in the\nnext sections we used the Planck 2015 likelihood, which is described in detail in [5]. The Planck\nlikelihood uses a hybrid approach with the combination of one likelihood dedicated to low-` and\n\n3Although there are other possible sources of B-modes connected with exotic cosmological models, like the\none which includes primordial magnetic fields.\n\nMagellan Workshop 2016 3\n\nPLANCK 2015 COSMOLOGICAL RESULTS\n\nMagellan Workshop 2016 73\n\n\n\nFigure 2: CMB anisotropy angular power spectra in temperature and polarization as measured\nby Planck 2015. The upper left panel is the temperature spectrum, the upper right is the\ntemperature-E-mode cross-correlations, the lower left panel is the E-mode and the lower right\npanel is the joint analyisis of Planck and BICEP2/Keck of B-mode polarization.\n\nFigure 3: Planck 2015 lensing results, the lensing potential is on the left panel; the reconstructed\nmatter distribution map on the right panel.\n\n4 Magellan Workshop 2016\n\nDANIELA PAOLETTI\n\n74 Magellan Workshop 2016\n\n\n\nParameter TT+lowP TT+lowP+lensing TT,TE,EE+lowP TT+lowP+lensing+ext\n\nΩbh\n2 0.02222 ± 0.00023 0.02226 ± 0.00023 0.02225 ± 0.00016 0.02227 ± 0.00020\n\nΩch\n2 0.1197 ± 0.0022 0.1186 ± 0.0020 0.1198 ± 0.0015 0.01184 ± 0.0012\n\n100θMC 1.04085 ± 0.00047 1.04103 ± 0.00046 1.04077 ± 0.00032 1.04106 ± 0.00041\n\nτ 0.078 ± 0.019 0.066 ± 0.016 0.079 ± 0.017 0.067 ± 0.013\n\nln(1010As) 3.089 ± 0.036 3.062 ± 0.029 3.094 ± 0.034 3.064 ± 0.024\n\nns 0.9655 ± 0.0062 0.9677 ± 0.0060 0.9645 ± 0.0049 0.9681 ± 0.0044\n\nH0 67.31 ± 0.96 67.81 ± 0.92 67.27 ± 0.66 67.90 ± 0.55\n\nTable 1: Parameter 68% confidence limits for the base ΛCDM model from Planck CMB power\nspectra, in combination with lensing reconstruction (”lensing”) and external data (”ext”,i.e.\nBAO+JLA+H0)[6].\n\nanother to high-`. The Planck low-` likelihood is a fully pixel-based likelihood with temperature\nand polarization with an `-range 2 < ` < 29 in TT , TE, EE, and BB. The likelihood is\nbased on the foreground-cleaned LFI maps at 70 GHz and the temperature map derived by the\ncomponent separation method Commander [5]. This likelihood is denoted as lowP and is based\nentirely on Planck data for both temperature and polarization. The Planck high-` likelihood\nis based on a Gaussian approximation and covers the `-range 30 < ` < 2500. It uses the half-\nmission cross-power spectra of the 100 GHz, 143 GHz, and 217 GHz channels. The likelihood\ntakes foregrounds and secondary anisotropies into account [5]. It is denoted as Planck TT , for\ntemperature only, or Planck TT , TE, EE, for temperature plus polarization. In addition to\nthe likelihood based on the CMB anisotropies, the Planck collaboration has also delivered a\nlikelihood based on the lensing potential with a conservative range in ell of 40 < ` < 400 [15]. In\naddition to the CMB based likelihood Planck uses also a compilation of other non-CMB datasets\ndenoted as ext which include: BAO acoustic oscillationd from 6dFGS+SDSS+BOSS LOWZ;\nsupernovae Ia “”Joint Light curve analysis from SNLS and SDSS, a prior on H0 = (73.9± 3.3)\nKm/sec/Mpc, for details see [6].\n\n3.1 ΛCDM\n\nThe standard cosmological model, the ΛCDM, is described by six cosmological parameters: the\nbaryon density ωb = Ωbh\n\n2, the cold dark matter density ωc = Ωch\n2 , the reionization optical\n\ndepth τreion, the ratio of the sound horizon to the angular diameter distance at decoupling θ,\nthe scalar amplitude ln(As1010), and the scalar slope ns. The constraints derived with Planck\n2015 likelihood are presented in Table 1. The ΛCDM is very well constrained and represents a\nvery good fit to the Planck data.\n\n3.2 Neutrinos\n\nThe baseline Planck model assumes a normal neutrino mass hierarchy with\n∑\nmν = 0.06 eV\n\ndominated by heaviest neutrino. The main effect of the mass of neutrinos is on large scale\nstructure, the CMB is only slightly affected by neutrinos with masses below 1 eV. This is due\nto the fact that the effect of neutrino mass is caused by their transition from relativistic to non-\nrelativistic, and becoming effectively an extra mass component for the CMB anisotropies, but\nthis transition takes place well after recombination for masses below the eV. There is anyway\n\nMagellan Workshop 2016 5\n\nPLANCK 2015 COSMOLOGICAL RESULTS\n\nMagellan Workshop 2016 75\n\n\n\nFigure 4: Constraints on neutrinos, on the first two panesl we shoe the posterior distribution of\nthe sum of the masses and then the 2D joint distribution of the mass and the Hubble constant.\nOn the third panel we show the 2D joint posterior for the number of effective neutrinos and\nthe Hubble parameter.\n\nan effect at recombination which affects the early Integrated Sachs Wolfe effect, caused by the\nchanges in the gravitational potential wells induced by the transition. If not much can be\nsaid with CMB anisotropies, the lensing is instead strongly affected by neutrino mass, in fact\nmassive neutrinos suppress the clustering of structures on scaler smaller than the horizon at the\nnon-relativistic transition affecting the lensing potential. Therefore the lensing becomes a very\npowerful tool to constrain neutrino masses. In Fig.4 we show the results for the neutrino mass\nconstraints. The 95% C.L. limits are\n\n∑\nmν < 0.72 eV for Planck TT+lowP;\n\n∑\nmν < 0.21 eV\n\nfor Planck TT+lowP+BAO;\n∑\nmν < 0.49 eV for Planck TT,TE,EE+lowP;\n\n∑\nmν < 0.17 eV\n\nfor Planck TT,TE,EE+lowP+BAO;\n∑\nmν < 0.68 eV for Planck TT+lowP+lensing;\n\n∑\nmν <\n\n0.59 eV for Planck TT,TE,EE+lowP+lensing. The larger constraints with the addition of the\nlensing likelihood are due to the fact that the lensing likelihood based on the lensing potential\nreconstruction prefers lower values for the amplitude of the lensing signal with respect to the\none derived with the CMB angular power spectra based likelihood and therefore the lensing\nlikelihood allows for larger masses. Another possibility to investigate the neutrinos with CMB\nanisotropies are the constraints on the number of effective neutrinos. In fact, an increased\nnumber of effective neutrinos with respect to the standard prediction Neff = 3.046 would\nlead to a faster expansion affecting both the Big Bang Nucleosynthesis predictions and the\ndamping tail of the CMB anisotropies (it causes the recombination to happen earlier assuming\nthe same size of the horizon as seen by Planck). The results are shown in the third panel of\nFig.4. The Planck results are perfectly consistent with Neff = 3.046 with single constraints\ngiven by (68% C.L.): Neff = 3.13 ± 0.32 Planck TT+LowP; Neff = 3.15 ± 0.23 Planck\nTT+LowP+BAO ;Neff = 2.99 ± 0.20 Planck TT,TE,EE+LowP; Neff = 3.04 ± 0.18 Planck\nTT,TE,EE+LowP+BAO. The constraints for the combination of variable mass and number\nof effective neutrinos confirms the results of the separate cases:\n\n∑\nmν < 0.32 eV and Neff =\n\n3.2± 0.5 Planck TT+LowP+lensing+BAO. If we add a massive sterile neutrino in addition to\nthe baseline three we have:\n\n∑\nmν < 0.52 eV and Neff < 3.7 Planck TT+LowP+lensing+BAO\n\nand\n∑\nmν < 0.38 eV and Neff < 3.7 Planck TT+LowP+lensing+BAO when restricting the\n\nprior on mthermal\nsterile < 2 eV instead of 10 eV of the first result.\n\n6 Magellan Workshop 2016\n\nDANIELA PAOLETTI\n\n76 Magellan Workshop 2016\n\n\n\n3.3 Inflation\n\nThe inflation is a phase predicted in the standard cosmological model, in which the Universe un-\nderwent an exponential expansion [17]. Several models of inflation have been proposed through\nthe years and each has its own predictions on the CMB anisotropies. Two of the main pre-\ndictions of the most common inflationary models are a spectral index different from the scale\ninvariance and the generation of tensor perturbations, therefore a tensor to scalar ratio r dif-\nferent from zero. In the left panel of Fig.5 we show the Planck 2015 and the combination of\nPlanck+BICEP 2/KEck joint analysis results on the joint posterior distribution ns and r com-\npared with the predictions of some inflationary models. We note how Planck 2015, assuming a\nΛCDM or ΛCDM+tensor model, excludes the scale invariance ns = 0.9655±0.0062 (68% C.L.)\nwith Planck TT+LowP, and gives strong upper limits on r < 0.11 Planck TT+LowP, reducing\nthe allowed region of inflationary models [8].\n\n3.4 Curvature, Dark Energy and Primordial Magnetic Fields\n\nTwo of the main extesions to the standard cosmological model consider models of dark energy\nwhich are not cosmological constants and the possibility to have non-zero curvature. In the first\ncase Planck 2015 results are perfectly compatible with a dark energy in the form of a cosmo-\nlogical constant, this means that the parameter of the equation of state is w = −1. The Planck\nresults on the dark energy are: w = −1.54+0.62\n\n−0.50 Planck TT+LowP; w = −1.023+0.091\n−0.096 Planck\n\nTT+LowP+ext; w = −1.006+0.085\n−0.091 Planck TT+LowP+lensing+ext; w = −1.019+0.075\n\n−0.080 Planck\nTT,TE,EE+LowP+lensing+ext. Also for the curvature Planck 2015 results are perfectly com-\npatible with the standard cosmological model, namely with a flat geometry: Ωk = −0.0512+0.049\n\n−0.055\nPlanck TT+LowP;Ωk = −0.040+0.038\n\n−0.041 Planck TT,TE,EE+LowP;Ωk = −0.005+0.016\n−0.017 Planck\n\nTT+LowP+lensing;Ωk = 0.0000 ± 0.005 Planck TT+LowP+lensing+BAO. The slight prefer-\nence for negative values, closed universe, is due to a geometrical degeneracy and in fact note\nhow adding the lensing and the BAO which break this degeneracy, brings the value in perfect\ncompatibility with a flat geometry. The CMB allows to strongly constrain also more exotic\ncosmological models and in particular can give strong constrain to the one which considers\nthe presence of primordial magnetic fields (PMF). PMF can naturally arise in the early Uni-\nverse thanks to mechanisms related to inflation or phase transitions and may represent the\nprogenitors of the fields we observe on large scale structures. PMF have several impact on\nCMB anisotropies [19, 18] and therefore CMB anisotropies can give strong constraints on PMF\namplitude.Planck 2015 constraints are shown in the right panel Fig.5. The upper limits (95%\nC. L.) are: B1Mpc < 4.4 nG Planck TT+lowP for generic spectral index; BnB=2\n\n1Mpc < 0.011 nG;\n\nBnB=1\n1Mpc < 0.1 nG; BnB=0\n\n1Mpc < 0.5 nG; BnB=−1.5\n1Mpc < 4.8 nG; BnB=−2.5\n\n1Mpc < 2.4 nG; BnB=−2.9\n1Mpc < 2.0\n\nnG [7].\n\n3.5 Conclusions\n\nThe Planck results represent a milestone in cosmology. The amount of data released is a legacy\nwhich will be exploited for years and years to come.Planck has confirmed that the ΛCDM model\nrepresents a very good fit to the CMB data, and has measured the cosmological parameters\nthat describes it with unprecedented accuracy. But there are still a lot of challenges for the\nCMB future, the measurements of the B-mode polarization on large angular scales, the large\nscale anomalies etc. The final Planck release is expected for late 2016.\n\nMagellan Workshop 2016 7\n\nPLANCK 2015 COSMOLOGICAL RESULTS\n\nMagellan Workshop 2016 77\n\n\n\nFigure 5: Constraints on inflation on the left and the constraints on primordial mag-\nnetic fields on the right: solid curve is Planck TT+lowP, compensated modes; the red\ndashed is Planck TT,TE,EE+lowP compensated; the green dotted line is PLanck TT+lowP\ncompensated+passive modes; the blue dot-dashed is the Planck TT,TE,EE+lowP compen-\nsated+passive modes. .\n\n4 Acknowledgments\n\nD.P. acknowledges the support by ASI/INAF Agreement 2014-024-R.0 for the Planck LFI\nActivity of Phase E2.\n\nReferences\n[1] D. J. Fixsen, The Astrophys. J., 707, 916 (2009).\n\n[2] Hu W., White M., NewA, 2, 323 (1997).\n\n[3] Planck Collaboration, arXiv:1502.01582 (2015).\n\n[4] Planck Collaboration, arXiv:1502.05956 (2015).\n\n[5] Planck Collaboration, arXiv:1507.02704 (2015).\n\n[6] Planck Collaboration, arXiv:1502.01589 (2015).\n\n[7] Planck Collaboration, et al., arXiv:1502.01594 (2015).\n\n[8] Planck Collaboration, et al., arXiv:1502.02114 (2015).\n\n[9] J.A. Tauber, N. Mandolesi, J.-L. Puget, et al. A&A, 520, A1 (2010).\n\n[10] Planck Collaboration, A&A, 571, A1 (2014).\n\n[11] Planck Collaboration, et al., arXiv:1502.01585 (2015).\n\n[12] Planck Collaboration, et al., arXiv:1502.01587 (2015).\n\n[13] Planck Collaboration, et al., arXiv:1605.02985 (2016).\n\n[14] BICEP2/Keck and Planck Collaborations, Ade, P. A. R., Aghanim, N., et al., Physical Review Letters,\n114, 101301,(2015).\n\n[15] Planck Collaboration, et al., arXiv:1502.01591 (2015).\n\n[16] Lewis A., Bridle S., ascl.soft, ascl:1106.025 (2011).\n\n[17] Liddle A. R., Lyth D. H.,cils.book, 414 (2000).\n\n[18] Paoletti D., Finelli F., 2011, PhRvD, 83, 123533\n\n[19] Paoletti D., Finelli F., Paci F., 2009, MNRAS, 396, 523\n\n8 Magellan Workshop 2016\n\nDANIELA PAOLETTI\n\n78 Magellan Workshop 2016\n\n\n\nFigure 1: The Orion Nebula (M42) as photographed using Hamburg Observatory’s Great\nSchmidt Mirror on 9th March 1959. Credit: Digital Plate Archives of Hamburger Sternwarte\n\n\n\nFigure 2: The nearby galaxy in Andromeda (M31) as photographed using Hamburg Observa-\ntory’s Great Schmidt Mirror on 31st August 1959. Credit: Digital Plate Archives of Hamburger\nSternwarte\n\n\n\nFigure 3: The Comet Mrkos, officially discovered by Czech astronomer Antonin Mrkos on 2nd\nAugust 1957. It probably had been seen by two others up to four days prior, but neither\nreported their sighting. Credit: Digital Plate Archives of Hamburger Sternwarte\n\n\n\nFigure 4: The Moon. Photograph taken with Hamburg Observatory’s Great Refractor tele-\nscope in the night of 27th/28th February 1929. Credit: Digital Plate Archives of Hamburger\nSternwarte\n\n\n\nFigure 5: The Moon photographed using the Great Refractor on 17th March 1929. Credit:\nDigital Plate Archives of Hamburger Sternwarte\n\n\n\nFigure 6: Hamburg astronomers gathered in the library to discuss results. Note Otto Heckmann\nand Richard Schorr in the center. Credit: Photographic Archive of the Hamburg Observatory\n\n\n\nFigure 7: Hamburg astronomers preparing instruments for the observation of a solar eclipse\nin Russia. In the 1920s and 1930s multiple expeditions to various locations in South America\nand Asia were conducted to study eclipses or objects not visible from the northern hemisphere.\nCredit: Photographic Archive of the Hamburg Observatory\n\n\n\nFigure 8: Solar eclipse in 1912 as photographed using Hamburg Observatory’s Lippert Astro-\ngraph telescope. Credit: Digital Plate Archives of Hamburger Sternwarte\n\n\n\nThe SNO+ experiment: current status and fu-\n\nture prospects.\n\nValentina Lozza1 for the SNO+ Collaboration\n\n1Institut für Kern- und Teilchenphysik, TU Dresden, Zellescher Weg 19, 01069 Dresden, Ger-\nmany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/5\n\nSNO+ is a multi-purpose neutrino experiment whose main physics goal is the search for\nthe neutrinoless double-beta (0νββ) decay of 130Te. With an initial loading of 0.5% (by\nweight) of natural tellurium SNO+ is expected to reach a sensitivity on the 0νββ lifetime\nof the order of 1026 years at 90% C.L. in a 5-year run. Along with the 0νββ decay search,\nSNO+ has the capability to measure the low energy solar neutrinos, like pep and CNO\nneutrinos. It has also an extraordinary opportunity to do the first measurement of the\nsupernova νx energy spectrum.\n\n1 Introduction\n\nThe SNO+ experiment is reusing the Sudbury Neutrino Observatory detector which is located\nat a depth of 2 km in the Vale’s Creighton mine in Sudbury, Canada. The deep underground\nlocation, the large volume, and the high radio-purity of the materials used allows SNO+ to\nexplore several neutrino physics topics.\n\nThe primary goal of SNO+ is the search for the Dirac or Majorana nature of the neutrinos\nvia searching for the neutrinoless double-beta (0νββ) decay of 130Te. The neutrinoless double-\nbeta decay is an extremely rare nuclear process that can only happen if neutrinos are their own\nantiparticles, i.e. they have a Majorana mass component. With an initial loading of 0.5% of\ntellurium by weight, SNO+ aims to reach a sensitivity on the effective Majorana neutrino mass\nsmaller than 100meV, exploring the top of the inverted hierarchy region in five years.\n\nThe low energy threshold of the SNO+ experiment, the deep underground location and the\nhigh radiopurity allows the measurement of the low-energy solar neutrinos, like pep, 8B, and\nCNO neutrinos. The measurement of the pep neutrino flux and of the spectral shape of the\n8B-neutrinos will shed like on oscillation models, while the measurement of the CNO neutrino\nflux can solve the solar metallicity problem.\n\nAlong with these goals SNO+ can measure antineutrinos from nuclear reactors and the\nEarth’s crust and mantle, can watch for neutrinos and antineutrinos from core collapse su-\npernovae, and has the potential to search for exotics physics, like invisible nucleon decay, and\naxion-like particles.\n\nThis article is structured as follows. In Section 2 the SNO+ detector is described together\nwith the most recent upgrades, and the liquid scintillator and tellurium purification processes.\nIn Section 3 the physics goals, the expected phases of the experiment and the run plan is\npresented. In Sections 4 to 6 the physics program of SNO+ is described: the neutrinoless\n\nMagellan Workshop 2016 1Magellan Workshop 2016 87\n\n\n\ndouble-beta decay search (Section 4), the measurement of low energy solar neutrinos (Section\n5), and the watch for supernova neutrinos and antineutrinos (Section 6). The conclusion will\nfollow at the end.\n\n2 The SNO+ Experiment\n\nThe SNO+ experiment is located at a depth of 5890 ± 94 m.w.e. in the SNOLAB underground\nlaboratory [1]. The flat overburden provides a shield against the cosmic muons, reducing the\nmuon flux to 63 muons per day passing through a 8.3m radius circular area. SNO+ will\nreuse the SNO detector infrastructure [2, 3] which consists of a spherical acrylic vessel of 12m\ndiameter and 5.5 cm thickness suspended in a cavity excavated in the rock via a system of high\npurity ropes. The vessel will be filled by about 780 tonnes of liquid scintillator and is viewed\nby 9300 8 inch PMTs placed on a geodesic stainless steel structure (PSUP) of 19m diameter.\nEach PMT is equipped with a light concentrator for an overall coverage of 54%. The volume\nbetween the acrylic vessel and the PSUP and the one outside the PSUP is filled with high\npurity water to shield against the radioactivity coming from the rock and the PMTs. Due to\nthe lower density of the liquid scintillator (LS) contained in the acrylic vessel compared to the\none of the surrounding water a new system of high purity ropes has been installed on the top\nof the vessel and anchored to the cavity floor to offset the LS buoyancy [4].\n\n2.1 Liquid scintillator\n\nThe SNO+ liquid scintillator consists of linear alkylbenzene (LAB) as a solvent and 2 g/L of\n2,5-diphenyloxazole (PPO) as a fluor. The LAB+PPO mixture has been selected among others\ndue to its chemical compatibility with the acrylic vessel, its high light yield of more than 10000\nphotons per MeV, its transparency and good optical properties (long attenuation and scattering\nlengths), its linear response in energy, and its long term stability. Additionally, the fast decay\ntime allows to discriminate between alpha-like and beta-like signals.\n\nDespite the high purity levels available directly from the manufacturer, a further purification\nof the liquid scintillator is necessary in order to achieve a U- and Th-chain radiopurity close to\nthe one of Borexino and KamLAND, i.e. less than 10−17 g/gLAB [5]. The scintillator purification\nplant of SNO+ is fully described in [6, 7]. Heavy metals and optical impurities are removed by\na multi-stage distillation and high temperature flash vacuum distillation. Gases and residual\nwater are then removed by a N2/steam gas stripping process. Once the SNO+ detector is filled\nwith LAB+PPO the mixture can be further purified in-situ by recirculating the scintillator\n(about four days for the entire volume) through a rotating-stage liquid-liquid extraction column\nand metal scavengers. The liquid scintillator purification plant has been fully installed at the\nSNOLAB depth and is currently undergoing commissioning.\n\n2.2 Te-loading\n\nTo search for the 0νββ decay SNO+ will load tellurium into the liquid scintillator. The SNO+\ncollaboration has developed a new technique to load several tonnes of tellurium into LAB\nmaintaining a high light emission level and good optical properties. In the initial phase SNO+\nwill load 3.9 tonnes of natural tellurium (a concentration of 0.5% by weight) by forming an\norganometallic compound of tellurium and butanediol which is soluble in LAB scintillator. A\n\n2 Magellan Workshop 2016\n\nVALENTINA LOZZA\n\n88 Magellan Workshop 2016\n\n\n\nsecondary wavelength shifter (bis-MSB) is added to better match the PMT quantum efficiency.\nThe currently predicted light yield in SNO+ for the 0.5% Te-loaded cocktail is 390Nhits (de-\ntected photoelectron hits) per MeV of energy.\n\nThe purification technique for tellurium is described in [8]. It has been designed to remove\nboth the U- and Th-chain impurities and the isotopes produced by cosmogenic neutron and\nproton activation of the material during handling and storage on the earth’s surface. The\npurification plant for tellurium consists of two stages. Both the stages are expected to hap-\npen underground. A first double-pass acid recrystallization (Stage I) reduces the U/Th and\ncosmogenic-induced isotope level of a factor larger than 104. In the second purification stage\n(Stage II) tellurium is dissolved in water at 80◦ C and left to cool to recrystallize without further\nrinsing. This stage will further reduce the impurity level of a factor larger than 100.\n\n2.3 Detector Upgrades\n\nIn addition to the installation of the hold-down rope system several detector upgrades have\nbeen done in the latest years. The read-out boards and the data acquisition system of SNO\nhave been upgraded with a higher bandwidth one to cope with the higher rate and lower energy\nevents expected in SNO+. The trigger system has been improved for a more flexible calibration\ninterface. The electronics and the trigger system have been tested in runs with the detector\nempty and partially filled with water. A new calibration hardware has been designed which\nconsists of a set of radioactive sources (beta, gamma, alpha and neutron) and a set of optical\nsources. The optical calibration system consists of deployable sources and a fixed external\nsystem of optical fibers attached to the PSUP as described [9]. The SNO cover gas system,\na sealed system of bags filled with high-purity nitrogen that shields the detector against the\nradon contained in the mine air, has been upgraded for a radon reduction of about 105.\n\n3 Physics Goals and Run Plan\n\nThe main goal of the SNO+ experiment is the search for the 0νββ decay of 130Te. However,\ndue to the deep underground location and the high purity of the materials used, SNO+ has\nthe potential to explore other physics topics: the measurement of the low energy pep and\nCNO neutrino flux, the measurement of the spectral shape of the 8B-neutrinos, the watch for\nsupernova (SN) neutrinos and antineutrinos, and the measurement of antineutrinos produced\nin nuclear reactors and in the Earth’s crust and mantle.\n\nCurrently, there are planned three main phases for the experiment. During the initial phase\nthe detector will be filled with about 905 tonnes of ultra-pure water (1, water phase). This phase\nwill start by the end of 2016 and last for a few months. In this phase the detector performance\nand the PMT response will be checked. At the same time SNO+ will search for exotic physics\nevents, like the invisible nucleon decay, and will watch for SN-neutrinos.\n\nThe water phase will be followed by a pure liquid scintillator phase (2) where the detector\nis filled with about 780 tonnes of LAB+PPO. Also this phase will last few months. The physics\ntopics covered are the measurement of the low energy solar neutrinos, the watch for SN neutri-\nnos, and the measurement for reactor and geo- antineutrinos. This phase will be also used to\nmeasure the liquid scintillator background and verify the optics model.\n\nTellurium will be loaded into the detector starting from the end of 2017 with the beginning\nof the data taking expected for the summer of 2018. The double-beta decay phase (3) will last\n\nMagellan Workshop 2016 3\n\nTHE SNO+ EXPERIMENT: CURRENT STATUS AND FUTURE PROSPECTS.\n\nMagellan Workshop 2016 89\n\n\n\n(MeV)\nββ\n\neffT\n\n2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3\n\nC\no\n\nu\nn\n\nts\n/5\n\n y\n/2\n\n0\n k\n\neV\n b\n\nin\n\n0\n\n10\n\n20\n\n30\n\n40\n\n50 (200 meV)ββν0\n\nββν2\n\nU Chain\n\nTh Chain\n\n, n)α(\n\nExternal\n\nESνB \n8\n\nCosmogenic\n\nFigure 1: Energy spectrum plot (stacked) of all backgrounds and an hypothetical 0νββ signal\ncoresponding to mββ = 200meV. The plot is shown for 5 years live-time, 0.5% Te-loading and a\nlight yield of 390Nhits/MeV. A fiducial volume of 20% has been applied. External backgrounds\nare further reduced by a factor of two, while 214BiPo and 212BiPo are reduced by >99.99% and\n98%, respectively. Tββ is the effective kinetic energy.\n\nabout 5 years. Simultaneously to the 0νββ search the detector will be live for a potential SN,\nand the geo- and reactor antineutrinos can be observed.\n\n4 130Te neutrinoless double-beta decay search\n\nThe observation of the 0νββ decay would demonstrate lepton number violation, and shed light\non the ordering of the neutrino masses, both intriguing open questions in the neutrino research\narea. The 0νββ decay is a nuclear process in which two neutrons are converted into two protons\nand two electrons without the emission of the neutrinos. It can be seen as two simultaneous\nβ-decays in which the neutrinos from the two weak vertices mutually annihilate. The signature\nof the decay is a peak at the Q-value of the reaction in the summed energy spectrum of the two\nelectrons. The measured observable is the reaction’s rate, from which it is derived the half-life\nof the decay. The effective Majorana neutrino mass, mββ, is then extracted from the half-life\nas described in [10].\n\nIn SNO+ the search for the 0νββ decay is done by loading large amounts of 130Te into the\ndetector volume. 130Te has a large isotopic abundance of 34.08%, which allows to load several\ntonnes of the isotope without the need of enrichment. The Q-value of the decay (2527.518\n± 0.013keV [11]) is above many of the U- and Th-chain decays’ endpoints. The half-life of\nthe associated 2νββ decay (7.0±0.9(stat)±1.1(syst)×1020 yr [12]), one of the main irreducible\nbackgrounds in the 0νββ decay search, is among the longest of all the 0νββ decay candidates.\nCurrently, loadings up to few percent (by weight) of natural tellurium in LAB have been\nsuccessfully obtained with very good optical properties, high light yield and no inherent optical\n\n4 Magellan Workshop 2016\n\nVALENTINA LOZZA\n\n90 Magellan Workshop 2016\n\n\n\nEnergy (MeV)\n0.3 0.4 0.5 0.6 1 2 3 4 5 6\n\nE\nv\ne\n\nn\nts\n\n /\n 0\n\n.0\n2\n\n M\ne\n\nV\n /\n\n y\ne\n\na\nr\n\n1\n\n10\n\n210\n\n310\n\n410\n\n510\n\n610\n\nSum\n\nB\n8\n\nBe\n7\n\nCNO\n\npep\n\nU chain\n\nTh chain\n\nAr\n39\n\nBi\n210\n\nC11\n\nKr\n85\n\nPo\n210\n\nFigure 2: Expected solar neutrino fluxes and backgrounds as detected by the SNO+ experiment.\nThe background levels are assumed to be similar to the one initially achieved by Borexino. A\nfiducial volume cut of 5.5m and a light yield of 400Nhits/MeV are assumed. The 214BiPo\nevents are rejected by 95% using delayed coincidences, while a 95% reduction is applied to the\n210Po and remaining 214Po alpha signals. U and Th-chain backgrounds have a constrain of 7%\nand 25%, respectively.\n\nabsorption line in the visible wavelength range.\nThe expected energy spectrum for a 0.5% Te loading and a five-year live-time is shown\n\nin Figure 1. A fiducial volume (FV) cut of 3.5m, corresponding to 20% of the total volume,\nhas been applied. The region of interest (ROI) for the 0νββ decay search extends from -0.5σ\nto +1.5σ around the Gaussian signal peak. The asymmetric ROI considerably reduces the\nbackground events from the 2νββ and the low energy U- and Th-chain decays, leaving as main\nbackground contribution the 8B-ν elastic scattering (irreducible flat background). 214BiPo (U\nchain) and 212BiPo (Th chain) events are rejected using a delayed coincidence tagging and,\nfor the events happening in the same trigger window, pile-up cuts [1]. The 0νββ decay signal\n\nshown in Figure 1 corresponds to a mββ = 200meV, or to a T0νββ\n1/2 ∼ 1025 yr using IBM-\n\n2 nuclear matrix elements [13]. The expected sensitivity of SNO+ in 5 years of running is\n\nT0νββ\n1/2 > 1.96× 1026 yr at 90% CL, corresponding to a limit on the effective Majorana neutrino\n\nmass of 38 – 92meV (G = 3.96 × 10−14 yr−1 [14] and gA = 1.269), depending on the nuclear\nmatrix elements used.\n\n5 Low-energy Solar Neutrinos\n\nThe solar neutrino spectrum as detected by SNO+ is shown in Figure 2 together with the main\nbackground sources during the pure scintillator phase. With a liquid scintillator purity similar\nto the one achieved by Borexino in its Phase I [5], SNO+ can detect pep and CNO neutrinos with\n\nMagellan Workshop 2016 5\n\nTHE SNO+ EXPERIMENT: CURRENT STATUS AND FUTURE PROSPECTS.\n\nMagellan Workshop 2016 91\n\n\n\na sensitivity of few percent. Additionally, a spectral shape measurement of the 8B neutrinos\ncan be performed also during the 0νββ phase for energies above the 130Te endpoint.\n\n5.1 Pep neutrinos\n\nThe pep reaction in the Sun produces monoenergetic neutrinos (1.44MeV) which have very\nwell predicted flux (1.2% uncertainty) constrained by the Sun luminosity. A measurement of\nthese neutrinos has the potential to probe the transition region between the vacuum oscillation\n(E≤ 1MeV) and the matter-enhanced oscillation (E≥ 1MeV) energy regions of the νe survival\nprobability. The shape of the survival probability in the transition region is sensitive to alternate\nmodels of neutrino-matter interactions, such as flavor changing neutral currents or mass-varying\nneutrinos. A minimum precision of 10% is necessary to discriminate among different models.\nTo improve the sensitivity to non standard neutrino interactions, the spectral shape of 8B-νs\ncan be used. Due to their production closer into the core of the Sun, the effect of new physics\nis enhanced for these neutrinos.\n\nWith a background level similar to the one achieved in Borexino Phase I, MonteCarlo\nsimulations suggest that, with one year of data and a 50% fiducial volume cut, the expected\nuncertainty on the pep neutrinos is 8.9% (13% in six months), while the expected uncertainty\non the 8B neutrinos is about 7% (10% in 6 months).\n\n5.2 CNO Neutrinos\n\nRecent improvements in the solar atmospheric modelling suggest a photosphere’s metallicity\n(abundance of elements heavier than helium) about 30% lower than what was predicted by the\nolder but quite successful model. This broke the excellent agreement between the solar model\ncalculations and helioseismology, opening the question about the homogeneous distribution of\nthe elements in the Sun. The prediction for the CNO neutrino flux depends linearly on the core\nmetallicity and thus can shed light on the solar metallicity problem. Additionally, the CNO-ν\nflux can be further constrained by a precision measurement of the 8B-ν flux as it depends on\nthe same environmental factors.\n\nThe major background for the CNO neutrino flux measurement are the 210Bi decays (Q-\nvalue = 1.16MeV). The shape of this decay is very similar to the CNO-ν one making the\ndiscrimination difficult. The separation can be achieved by an ex-situ constraint on the 210Bi\ndecay rate or using observable different from energy. In one year of data the expected uncer-\ntainty on the CNO+210Bi linked data is 4.4% (6.5% in six months). The CNO flux alone has\na predicted uncertainty of 15%.\n\n6 Supernova Neutrinos and Antineutrinos\n\nThe high radiopurity of SNO+ and its deep underground location makes it one of the most\npromising experiments for the detection of neutrinos from core collapse supernovae (CCSNe).\nCCSNe are expected to emit more than 99% of their energy via neutrinos of all flavours and\ntype. A measurement of these fluxes is expected to shed light on the explosion mechanism.\nCurrent models estimate than about half of the neutrinos are emitted in the first second of the\nCCSN’s burst [15].\n\n6 Magellan Workshop 2016\n\nVALENTINA LOZZA\n\n92 Magellan Workshop 2016\n\n\n\nEν (MeV)\n\n25 30 35 40 45 50 55 60 65 70\n\nC\no\nu\nn\nts\n\n/\n(b\nin\n\n1\ns\n0\n.4\n5\nk\nt)\n\n0\n\n5\n\n10\n\n15\n\n20\n\n25\n\n30\n\n35\n\n40\n\n45\n\n50\n\ntrue\n\nreconstructed\n\n(black : σstat, blue : σtot)\n\nbest fit\n\nFigure 3: Supernova (SN) neutrino energy distribution for the ν-p ES interaction in the SNO+\nexperiment. A FV cut of 5.5m and a detector threshold of 0.2MeV have been applied. Shown\nare the true, reconstructed and best fit energy distribution of the sum of νe, νe, and νx emitted\nin the first second of the burst for a 10 kpc away SN with a total 3×1053 erg energy ejected\nin the form of neutrinos. The uncertainties shown are statistical (black) and total (blue). The\nsystematic uncertainty which includes the ν-p ES cross section, the number of target protons,\nthe proton quenching parameter, the detector’s energy resolution, and the spectral νe and νe\nparameters, is too small to be resolved. The best fit spectrum shows excellent agreement.\n\nSNO+ has the potential to measure both the averaged energy as well as the total elec-\ntromagnetic energy of the neutrinos emitted in the CCSN. The possible interaction channels\nduring the pure liquid scintillator phase are summarized in [1]. For a 10 kpc away SN, with\n3×1053 erg of binding energy released in form of neutrinos (equally partitioned among all six\nflavours and types), SNO+ expects to detect about 195 events via inverse beta decay (IBD)\nreactions, and about 119 events via elastic scattering (ES) on protons (for an energy threshold\nabove 0.2MeV) with a 5.5m FV cut. The reconstructed energy spectrum of νe, νe, and νx\n(sum of νµ, νµ, ντ , and ντ ) neutrinos emitted in the first second of the CCSN and detected by\nν-p ES reaction is shown in Figure 3, together with the true neutrino spectrum. The recon-\nstructed energy spectrum is obtained from the unfolded proton energy, taking into account the\nnon-linear quenching of the proton energy and the finite detector resolution [1].\n\n7 Conclusion and Outlook\n\nSNO+ is a large-scale liquid scintillator experiment whose primary goal is the search for the\nneutrinoless double-beta decay of 130Te. During the initial Te-phase about 3.9 tonnes of nat-\nural tellurium are loaded into the liquid scintillator by which SNO+ is expected to reach a\nsensitivity on the effective Majorana neutrino mass on the top of the inverted hierarchy region.\n\nMagellan Workshop 2016 7\n\nTHE SNO+ EXPERIMENT: CURRENT STATUS AND FUTURE PROSPECTS.\n\nMagellan Workshop 2016 93\n\n\n\nAdditionally, due to the low energy threshold, the deep underground location and the high\npurity of the materials used SNO+ is also able to measure low energy solar neutrinos, like pep\nand CNO neutrinos. Furthermore, SNO+ can provide the first measurement of the spectral\nshape of the νx neutrinos ejected from a CCSN.\n\nSeveral upgrades have been made to the detector in order to reuse the old SNO vessel and\nelectronics. We expect to start the water phase data taking soon, with the tellurium loading\nforeseen for the end of the 2017.\n\nAcknowledgments\n\nThis work is supported by the German Research Foundation (DFG).\n\nReferences\n[1] S. Andringa, et al., The SNO+ Collaboration, Advances in High Energy Physics vol. 2016 6194250 (2016).\n\n[2] J. Boger et al., SNO Collaboration, Nuclear Instruments and Methods in Physics Research A vol. 449 pp.\n172–207 (2000).\n\n[3] N. Jelley, A. B. McDonald, and R.G. H. Robertson, Annual Review of Nuclear and Particle Science vol.\n59 pp. 431–465 (2009).\n\n[4] A. Bialek et al., Nuclear Instruments and Methods in Physics Research A vol. 827 pp. 152 – 160 (2016).\n\n[5] G. Alimonti et al., Borexino Collaboration, Nuclear Instruments and Methods in Physics Research A vol.\n609 pp. 58–78 (2009).\n\n[6] R. Ford, M. Chen, O. Chkvorets, D. Hallman, and E. Vázquez-Jáuregui, AIP Conference Proceedings vol.\n1338 pp. 183–194 (2011).\n\n[7] R. Ford for the SNO+ Collaboration, AIP Conference Proceedings vol. 1672 080003 (2015).\n\n[8] S. Hans, et al., Nuclear Instruments and Methods in Physics Research A vol. 795 pp. 132–139 (2015).\n\n[9] R. Alves et al., Journal of Instrumentation vol. 10 P03002 (2015).\n\n[10] K. Zuber, Contemporary Physics vol. 45 pp. 491–502 (2004).\n\n[11] M. Redshaw, B. J. Mount, E. G. Myers, and F. T. Avignone III, Physical Review Letters vol. 102 212502\n(2009).\n\n[12] R. Arnold et al., NEMO–3 Collaboration, Physical Review Letters vol. 107 062504 (2011).\n\n[13] J. Barea, J. Kotila, and F. Iachello, Physical Review C vol. 87 014315 (2013).\n\n[14] J. Kotila and F. Iachello, Physical Review C vol. 85 034316 (2012).\n\n[15] G. Pagliaroli, F. Vissani, M. L. Costantini, and A. Ianni, Astroparticle Physics vol. 31 pp. 163–176 (2009).\n\n8 Magellan Workshop 2016\n\nVALENTINA LOZZA\n\n94 Magellan Workshop 2016\n\n\n\nMeasurement of solar neutrino fluxes with Borex-\n\nino\n\nS. Marcocci24, M. Agostini24,22, K. Altenmüller22, S. Appel22, G. Bellini13, J. Benziger17,\nD. Bick4, G. Bonfini12, D. Bravo20, B. Caccianiga13, F. Calaprice16, A. Caminata3, M. Carlini12,\nP. Cavalcante12, A. Chepurnov23, D. D’Angelo13, S. Davini24, A. Derbin15, L. Di Noto3,\nI. Drachnev24, A. Etenko10, K. Fomenko2, A. Formozov13, D. Franco1, F. Gabriele12, C. Galbiati16,\nC. Ghiano3, M. Giammarchi13, M. Goeger-Neff22, A. Goretti16, M. Gromov23, C. Hagner4,\nE. Hungerford25, Aldo Ianni12, Andrea Ianni16, K. Jedrzejczak6, D. Jeschke22, M. Kaiser4,\nV. Kobychev 7, D. Korablev 2, G. Korga 12, D. Kryn 1, M. Laubenstein 12, B. Lehnert 26,\nE. Litvinovich10,11, F. Lombardi 12, P. Lombardi 13, L. Ludhova 13, G. Lukyanchenko 10,11,\nI. Machulin10,11, S. Manecki20, W. Maneschg5, E. Meroni13, M. Meyer4, L. Miramonti13,\nM. Misiaszek6,12, M. Montuschi21, P. Mosteiro16, V. Muratova15, B. Neumair22, L. Oberauer22,\nM. Obolensky1, F. Ortica14, M. Pallavicini3, L. Papp22, L. Perasso3, A. Pocar19, G. Ranucci13,\nA. Razeto12, A. Re13, A. Romani14, R. Roncin12,1, N. Rossi12, S. Schönert22, D. Semenov15,\nH. Simgen5, M. Skorokhvatov10,11, O. Smirnov2, A. Sotnikov2, S. Sukhotin10, Y. Suvorov27,10,\nR. Tartaglia 12, G. Testera 3, J. Thurn 26, M. Toropova 10, E. Unzhakov 15, A. Vishneva 2,\nR.B. Vogelaar20, F. von Feilitzsch22, H. Wang27, S. Weinz28, J. Winter28, M. Wojcik6, M. Wurm28,\nZ. Yokley20, O. Zaimidoroga2, S. Zavatarelli3, K. Zuber26, and G. Zuzel6\n\n1AstroParticule et Cosmologie, Universitè Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observa-\ntoire de Paris, Sorbonne Paris Citè, 75205 Paris Cedex 13, France\n2Joint Institute for Nuclear Research, 141980 Dubna, Russia\n3Dipartimento di Fisica, Università degli Studi e INFN, Genova 16146, Italy\n4Institut für Experimentalphysik, Universität Hamburg, 22761 Hamburg, Germany\n5Max-Planck-Institut für Kernphysik, 69117 Heidelberg, Germany\n6M. Smoluchowski Institute of Physics, Jagiellonian University, 30059 Krakow, Poland\n7Kiev Institute for Nuclear Research, 06380 Kiev, Ukraine\n8Institute for Theoretical and Experimental Physics, 117218 Moscow, Russia\n9Kepler Center for Astro and Particle Physics, Universität Tübingen, 72076 Tübingen, Ger-\nmany.\n10NRC Kurchatov Institute, 123182 Moscow, Russia\n11 National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409\nMoscow, Russia.\n12INFN Laboratori Nazionali del Gran Sasso, 67010 Assergi (AQ), Italy\n13Dipartimento di Fisica, Università degli Studi e INFN, 20133 Milano, Italy\n14Dipartimento di Chimica, Università e INFN, 06123 Perugia, Italy\n15St. Petersburg Nuclear Physics Institute NRC Kurchatov Institute, 188350 Gatchina, Russia\n16Physics Department, Princeton University, Princeton, NJ 08544, USA\n17Chemical Engineering Department, Princeton University, Princeton, NJ 08544, USA\n18Physics Department, Queen’s University, Kingston ON K7L 3N6, Canada\n19Amherst Center for Fundamental Interactions and Physics Department, University of Mas-\n\nMagellan Workshop 2016 1Magellan Workshop 2016 95\n\n\n\nsachusetts, Amherst, MA 01003, USA\n20Physics Department, Virginia Polytechnic Institute and State University, Blacksburg, VA\n24061, USA\n21Dipartimento di Fisica e Scienze della Terra Università degli Studi di Ferrara e INFN, Via\nSaragat 1-44122, Ferrara, Ital\n22Physik-Department and Excellence Cluster Universe, Technische Universität München, 85748\nGarching, Germany.\n23Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics, 119234 Moscow,\nRussia.\n24Gran Sasso Science Institute (INFN), 67100  L’Aquila, Italy.\n25Department of Physics, University of Houston, Houston, TX 77204, USA.\n26Department of Physics, Technische Universität Dresden, 01062 Dresden, Germany.\n27Physics and Astronomy Department, University of California Los Angeles (UCLA), Los An-\ngeles, California 90095, USA.\n28Institute of Physics and Excellence Cluster PRISMA, Johannes Gutenberg-Universität Mainz,\n55099 Mainz, Germany.\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/30\n\nSolar neutrinos have been of extraordinary importance for the discovery of neutrino oscil-\nlations and allow to study directly the Sun’s innermost core. The Borexino experiment,\nlocated in the Gran Sasso National Laboratory, is an ultra-pure liquid scintillator detector\nconceived for the real time spectroscopy of low energy solar neutrinos. We review Borex-\nino’s results and we discuss its upcoming future, since the precision era of solar neutrino\nmeasurements has just started.\n\n1 Solar neutrinos\n\nThe Standard Solar Model (SSM) is a theoretical framework which aims to describe the Sun,\nand particularly to study the energy production mechanisms, thus predicting the solar neutrino\nfluxes coming out of them [1]. The SSM has been continuously refined during the years, in-\ncluding results about cross section measurements of relevant nuclear reactions at low energy [2]\nand Helioseismology measurements [3, 4].\n\nThe Sun produces energy via different nuclear reactions. The so called pp chain, which\nresults in the fusion of 4 protons into 4He (see the left part of Fig. 1), is the set of nuclear\ntransitions which gives the most important contribution to the total energy production. The\nCNO cycle (see the right part of Fig. 1) involves a subdominant set of reactions in the Sun,\naccounting for ∼1% of the Sun’s energy release. However, the CNO cycle plays a key role\nin astrophysics, since it is the dominant source of energy in stars more massive than the Sun\nand in advanced evolutionary stages of Sun-like stars. A CNO neutrino measurement would\nconstrain the chemical composition of the Sun, leading to improved models for star formation\nand supernova explosions. A direct measurement of the CNO neutrino component could also\nsolve the long-standing solar metallicity problem [5], for which, basically, two different solutions\nare possible, namely the High Metallicity and Low Metallicity.\n\nThe theoretical solar neutrino spectra resulting both from the reactions of the pp chain and\nthe CNO cycle are shown on the left panel of Fig. 2.\n\n2 Magellan Workshop 2016\n\nSIMONE MARCOCCI et al.\n\n96 Magellan Workshop 2016\n\n\n\nFigure 1: Left: Schematic drawing of the pp reactions chain, [6]. Right: Sketch of the CNO\ncycle.\n\n2 The Borexino experiment\n\nThe study of low energy solar neutrinos with a ultra-pure liquid scintillator technique is relevant\nboth for testing the predictions of the Standard Solar Model and for investigating the MSW-\nLMA neutrino oscillation scenario in an energy range that is not accessible to water Cherenkov\ndetectors [7, 8]. In fact, up to a few years ago, spectroscopic measurements were performed\nby water Cherenkov detectors above ∼ 5 MeV and concerned only 8B neutrinos for less than\nthe 1% of the total flux. The bulk of neutrinos at lower energies were detected only with\nradiochemical experiments, unable to resolve the individual components. The original main\ngoal of Borexino [9, 10] was the precise measurement of the rate induced by the monochromatic\nelectron neutrinos (862 keV) produced by the electron capture decay of 7Be in the Sun. However,\nthe very high radio purity of the scintillator made it possible for Borexino to largely exceed the\nexpected performance and broaden the original physics program.\n\nBorexino is a liquid scintillator calorimeter [11] designed for the real-time observation of\nlow energy solar neutrinos and anti-neutrinos. Neutrinos are detected through their elastic\nscattering on electrons and by means of the consequent emission of scintillation light, while\nanti-neutrinos are detected through inverse beta decay on protons. A sketch of the experiment\nis shown in Fig. 2. It is located at the Gran Sasso National Laboratories (LNGS) in central Italy,\nat a depth of 3800 m w.e.. The inner part of the Borexino detector is an unsegmented stainless\nsteel sphere (SSS) that is both the container of the scintillator and the mechanical support of\nthe photomultipliers. Within this sphere, the active mass consists of 278 tons of pseudocumene\n(PC), doped with 1.5g/l of PPO. The scintillator is contained in a thin (125 µm) nylon Inner\nVessel (IV), 8.5 m in diameter. The IV is surrounded by two concentric PC buffers doped\nwith a light quencher. The scintillation light is collected by nominally 2212 photomultipliers\n\nMagellan Workshop 2016 3\n\nMEASUREMENT OF SOLAR NEUTRINO FLUXES WITH BOREXINO\n\nMagellan Workshop 2016 97\n\n\n\nFigure 2: Left Panel: Solar neutrino spectral components. Right Panel: Sketch of the\nBorexino detector.\n\n(PMTs) that are uniformly attached to the inner surface of the SSS. All but 384 PMTs are\nequipped with light concentrators that are designed to reject photons not coming from the\nactive scintillator volume, thus reducing the background due to radioactive decays originating\nin the buffer liquid or gammas from the PMTs. The 384 PMTs without concentrators are used\nto study this background, and to help identify muons that cross the buffer, but not the IV.\n\nThe SSS is supported by 20 steel legs and enclosed within a large tank (WT) that is filled with\nultra-pure water. The WT has a cylindrical base with a diameter of 18 m and a hemispherical\ntop with a maximum height of 16.9 m. The WT is a powerful shielding against external\nbackgrounds (γ rays and neutrons from the rock) and is also used as a Cherenkov muon counter\nand muon tracker. The muon flux, although reduced by a factor of 106 by the 3800 m w.e.\ndepth of the Gran Sasso Laboratory, is of the order of 1 m−2 h−1, corresponding to about 4000\nmuons per day crossing the detector. This flux is well above the Borexino requirements and a\nstrong additional reduction factor (about 104) is necessary. Therefore the WT is equipped with\n208 photomultipliers that collect the Cherenkov light emitted by muons in the water.\n\nThe Borexino scintillator has a light yield of ∼ 104 photons/MeV, resulting in ∼ 500 detected\nphotoelectrons/MeV. The fast time response of the scintillating mixture allows to reconstruct\nthe events position by means of a time-of-flight technique with a good precision. The signature\nof 7Be neutrinos is a Compton-like shoulder at 665 keV in the electron recoil spectrum. The\nenergy resolution (1σ) at the 7Be7 energy is as low as 44 keV (roughly 5% /\n\n√\nMeV).\n\n3 Results from Borexino\n\n7Be neutrinos The measurement of the flux of 7Be neutrinos was the primary goal of Borexino.\nThe first observation was published in the summer of 2007, after only 3 months of data taking.\nThe signal of the 7Be solar neutrinos is extracted from the data through a fit of the energy\nspectrum of the events collected within the fiducial volume. There is not special signature\nother then the shape of the spectrum itself (the Compton like shoulder with endpoint at 665\nkeV) that allows to distinguish the signal from background. The main background is due to\nthe radioactive decay of the isotopes 85Kr, 210Po, 210Bi, and 11C contaminating the scintillator,\n\n4 Magellan Workshop 2016\n\nSIMONE MARCOCCI et al.\n\n98 Magellan Workshop 2016\n\n\n\nbut their contributions can be disentangled very efficiently through the spectral fit, as shown\nin Fig. 3.\n\nFigure 3: Two example of fitted spectra for the 7Be solar neutrino measurement, [12]. On the\nleft, an analytic fit over the 290-1270 keV energy region to a spectrum obtained with statistical\nsubtraction of the α background is shown. The plot on the right presents a Monte Carlo based\nfit over the energy region 270-1600 keV to a spectrum where the α component has not been\nremoved. The fit results in the legends have units [counts/(day·100 ton)].\n\nThe rate of the neutrino-electron elastic scattering interactions from 862 keV 7Be solar\nneutrinos in Borexino resulted of 46.0 ± 1.5(stat) ± 1.5(syst) counts/(day·100 ton), [12]. This\ncorresponds to a νe-equivalent 7Be solar neutrino flux of (3.10 ± 0.15)×109 cm−2s−1 and, under\nthe assumption of νe transition to other active neutrino flavors, yields an electron neutrino\nsurvival probability of 0.51±0.07 at 862 keV. The no flavor change hypothesis is ruled out at\n5.0σ. Borexino also investigated the eventual day-night asymmetry in the 7Be solar neutrino\ninteraction rate. The measured asymmetry is 0.001 ± 0.012 (stat) ± 0.007 (syst) [13], in\nagreement with the prediction of MSW-LMA solution for neutrino oscillations. This result\ndisfavors MSW oscillations with mixing parameters in the LOW region at more than 8.5σ.\nThis region is, for the first time, strongly disfavored without the use of reactor anti-neutrino\ndata and therefore the assumption of CPT symmetry.\n\npep and CNO neutrinos Thanks to the development of novel data analysis techniques for\nthe rejection of the cosmogenic 11C and of external gammas (the main backgrounds in the 1-1.5\nMeV region), a first evidence of pep solar neutrinos could be obtained. Particularly, a “three-\nfold coincidence” vetoing method was developed [14]. It consists in eliminating the parts of the\nfiducial volume through which the muon tracks passed, possibly creating the 11C isotope. For\na further reduction of the background of positrons produced by the 11C decays, a pulse shape\ndiscrimination variable for β/β+ events has been developed (see e.g. the left side of Fig. 4). It\nexploits the different time distribution of the scintillation photons, due to the possible ortho-\npositronium formation [15]. The fit is then performed with a multivariate approach, fitting\nsimultaneously the energy spectrum, the pulse shape discrimination variable for β/β+ and the\nradial distribution of the events, in order to decouple the uniformly distributed neutrino-like\nevents from the external gammas. All the details can be found in Ref. [16].\n\nThe interaction rate of pep neutrinos in Borexino is 3.1 ± 0.6(stat) ± 0.3(syst) counts/(day·100\nton) [16]. The absence of the solar neutrino signal is disfavored at 99.97% C.L., while the ab-\n\nMagellan Workshop 2016 5\n\nMEASUREMENT OF SOLAR NEUTRINO FLUXES WITH BOREXINO\n\nMagellan Workshop 2016 99\n\n\n\nsence of the pep signal is disfavored at 98% C.L. . The strongest constraint on the CNO\nsolar neutrino interaction rate (<7.9 counts/(day·100 ton), at 95% C.L.[16]), could be obtained\nthrough the likelihood ratio test shown in the right part of Fig. 4.\n\nFigure 4: Left Panel: Experimental distribution (black) of the pulse shape parameter for\nβ/β+ discrimination. The best-fit distribution (black dashed) and the corresponding β (red)\nand β+ (blue) contributions are also shown. Left Panel: ∆χ2 profile obtained from likelihood\nratio tests between fit results where the pep and CNO neutrino interaction rates are fixed to\nparticular values (all other species are left free) and the best-fit result.\n\npp neutrinos The pp reaction in the core of the Sun is the keystone process for the energy\nproduction and is the source of the largest component of the neutrino flux. Its measurement\nis a major experimental milestone in solar neutrino physics, and paves the way to a deeper\nunderstanding of the Sun dynamics. This measurement was made possible by the very low\nradioactive background, particularly in 85Kr, achieved after an extensive purification campaign\nperformed in 2010-2011 and thanks to the extremely good performance of the detector as a\nwhole. Most of the remaining, unavoidable, background is due to 14C β-decays, whose end-\npoint is 156 keV, very close to the pp region of interest. To estimate its rate independently\nfrom the main analysis, a sample of data in which the event causing the trigger is followed by\na second event within the same trigger window (16 µs long, while physical events last 100-500\nns) has been analyzed. These second events are not subject to the trigger threshold, so they\nregister down to much lower energies. By fitting this spectrum against the known 14C spectral\nshape, the 14C rate in Borexino results of 40 ± 1 Bq per 100 tons.\n\nBecause of its high rate, 14C naturally generates a considerable amount of pile-up: two\nphysical events can occur so close to each other in time that the detector fails to resolve them.\nWith the above measurement of the 14C rate, the expected rate of 14C-14C pile-up is 100\nper day per 100 tons, comparable to the expected pp neutrino interaction rate. The pile-up\ncomponent can be determined using an independent, data-driven method: real triggered events\nwith no cuts are artificially overlapped with random data obtained from the ends of real trigger\nwindows, uncorrelated with the triggering event. The synthetic events are then reconstructed\nusing the same software used for real events, and selected with the same criteria. Using this\nmethod, it is possible to obtain the true rate and spectral shape of pile-up in the detector.\n\nThe solar pp neutrino interaction rate measured by Borexino is 144 ± 13(stat.) ± 10(syst.)\ncounts/(day·100 ton) [17]. The spectral fit result is summarized in Fig. 5. The absence of pp\n\n6 Magellan Workshop 2016\n\nSIMONE MARCOCCI et al.\n\n100 Magellan Workshop 2016\n\n\n\nsolar neutrinos is excluded with a statistical significance of 10σ. Once statistical and systematic\nerrors and the latest values of the neutrino oscillation parameters are taken into account, the\nmeasured solar pp neutrino flux is (6.6 ± 0.7) 1010 cm−2s−1, in accordance with the Standard\nSolar Model.\n\nFigure 5: The best fit of Borexino’s spectrum in the pp energy window is shown. The best-fit\npp neutrino component is shown in red, the 14C background in dark purple and the synthetic\npile-up in light purple. The large green peak is 210Po α-decays. The values of the parameters\n(in counts per day per 100 t) are in the inset above the figure.\n\n8B neutrinos Borexino could measure for the first time 8B solar neutrinos with an energy\nthreshold of only 3 MeV. The rate of 8B solar neutrino-induced electron scattering events above\nthis energy in Borexino is 0.217 ± 0.038(stat) ± 0.008(syst) counts/(day·100 ton [18], which\ncorresponds to a flux of (2.4±0.4) × 106 cm−2s−1, in good agreement with the measurements\nfrom SNO and SuperKamiokaNDE.\n\n4 Other Borexino measurements\n\nBorexino is a “big” ultra-pure calorimeter and, apart from solar neutrinos, could investigate\nmany other rare processes. Other results include the study of solar and other unknown anti-\nneutrino fluxes [19], a 5.9σ observation of geo-neutrinos with a 98% C.L. evidence of a signal\nfrom the mantle [20, 21], a measurement of neutrino velocity [22], searches for solar axions [23],\n\nMagellan Workshop 2016 7\n\nMEASUREMENT OF SOLAR NEUTRINO FLUXES WITH BOREXINO\n\nMagellan Workshop 2016 101\n\n\n\nexperimental limits on heavy neutrinos [24], and a test of the electric charge conservation and\nelectron decay [25].\n\n5 Outlook\n\nThe high impact of Borexino on neutrino physics is very well summarized by Fig. 6, where the\nsurvival probability of solar neutrinos is plotted as a function of the neutrino energy. For all\nthe solar neutrino components that Borexino measured, the trend is in good agreement with\nthe MSW-LMA prediction.\n\nFigure 6: Survival probability of electron-neutrinos produced by the different nuclear reactions\nin the Sun. All the experimental numbers are from Borexino’s measurements. The violet band\ncorresponds to the ±1σ prediction of the MSW-LMA solution.\n\nApart from the result on the pp solar neutrinos flux, which has been obtained after the\npurification campaign, all the other solar neutrino components were measured with data sets\ncoming from the so called Borexino Phase-I [26]. The Phase-II, which started right after the\npurification campaign at the end of Phase-I, is now close to its end, and this will hopefully lead\nto a general improvement of all the measurements of the solar neutrino fluxes. Particularly, the\nefforts of the Collaboration are pushing to achieve the best possible result (either an eventual\nmeasurement or an improvement of the limit) regarding the CNO flux determination. This\nwould be of extreme importance, since the CNO flux has not been observed yet, and could\nsolve the Solar Metallicity puzzle [5].\n\nThe Borexino detector will also be used to search for sterile neutrinos by means of and\nartificial anti-neutrino source in the framework of the SOX project [27].\n\n8 Magellan Workshop 2016\n\nSIMONE MARCOCCI et al.\n\n102 Magellan Workshop 2016\n\n\n\n6 Acknowledgements\n\nThe Borexino program is made possible by funding from the INFN (Italy); the NSF (USA);\nthe BMBF, DFG and MPG (Germany); the JINR, RFBR, RSC and NRC Kurchatov Institute\n(Russia); and the NCN: Grant No. UMO-2013/10/E/ST2/00180 (Poland). We acknowledge\nthe generous support of the Laboratori Nazionali del Gran Sasso (Italy) and that of the Gran\nSasso Science Institute. The SOX project is funded by ERC with grant ERC-AdG-2012 N.\n320873.\n\nReferences\n[1] A. M. Serenelli, Astrophys. Space Sci. 328, 13 (2010) doi:10.1007/s10509-009-0174-8.\n\n[2] F. Confortola et al. [LUNA Collaboration], Phys. Rev. C 75, 065803 (2007)\ndoi:10.1103/PhysRevC.75.065803.\n\n[3] W. J. Chaplin, A. M. Serenelli, S. Basu, Y. Elsworth, R. New and G. A. Verner, Astrophys. J. 670, 872\n(2007) doi:10.1086/522578.\n\n[4] S. Basu, W. J. Chaplin, Y. Elsworth, R. New, A. M. Serenelli and G. A. Verner, Astrophys. J. 655, 660\n(2007) doi:10.1086/509820.\n\n[5] C. Pena-Garay and A. Serenelli, arXiv:0811.2424 [astro-ph].\n\n[6] E. G. Adelberger et al., Rev. Mod. Phys. 83, 195 (2011) doi:10.1103/RevModPhys.83.195.\n\n[7] K. Abe et al. [Super-Kamiokande Collaboration], Phys. Rev. D 83, 052010 (2011).\n\n[8] B. Aharmim et al. [SNO Collaboration], Phys. Rev. C 81, 055504 (2010).\n\n[9] G. Alimonti et al. [Borexino Collaboration], Astropart. Phys. 16, 205 (2002).\n\n[10] C. Arpesella et al. [Borexino Collaboration], Astropart. Phys. 18, 1 (2002).\n\n[11] G. Alimonti et al. [Borexino Collaboration], Nucl. Instrum. Meth. A 600, 568 (2009).\n\n[12] G. Bellini et al., Phys. Rev. Lett. 107, 141302 (2011).\n\n[13] G. Bellini et al. [Borexino Collaboration], Phys. Lett. B 707, 22 (2012).\n\n[14] C. Galbiati, A. Pocar, D. Franco, A. Ianni, L. Cadonati and S. Schonert, Phys. Rev. C 71, 055805 (2005)\ndoi:10.1103/PhysRevC.71.055805\n\n[15] D. Franco, G. Consolati and D. Trezzi, Phys. Rev. C 83, 015504 (2011) doi:10.1103/PhysRevC.83.015504\n\n[16] G. Bellini et al. [Borexino Collaboration], Phys. Rev. Lett. 108, 051302 (2012).\n\n[17] G. Bellini et al. [Borexino Collaboration], Nature 512, no. 7515, 383 (2014).\n\n[18] G. Bellini et al. [Borexino Collaboration], Phys. Rev. D 82, 033006 (2010).\n\n[19] G. Bellini et al. [Borexino Collaboration], Phys. Lett. B 696, 191 (2011).\n\n[20] M. Agostini et al. [Borexino Collaboration], Phys. Rev. D 92, no. 3, 031101 (2015).\n\n[21] G. Bellini et al. [Borexino Collaboration], Phys. Lett. B 722, 295 (2013).\n\n[22] P. Alvarez Sanchez et al. [Borexino Collaboration], Phys. Lett. B 716, 401 (2012).\n\n[23] G. Bellini et al. [Borexino Collaboration], Phys. Rev. D 85, 092003 (2012).\n\n[24] G. Bellini et al. [Borexino Collaboration], Phys. Rev. D 88, no. 7, 072010 (2013).\n\n[25] M. Agostini et al. [Borexino Collaboration], Phys. Rev. Lett. 115, 231802 (2015).\n\n[26] G. Bellini et al. [Borexino Collaboration], Phys. Rev. D 89, no. 11, 112007 (2014).\n\n[27] G. Bellini et al. [Borexino Collaboration], JHEP 1308, 038 (2013).\n\nMagellan Workshop 2016 9\n\nMEASUREMENT OF SOLAR NEUTRINO FLUXES WITH BOREXINO\n\nMagellan Workshop 2016 103\n\n\n\n\n\nMonte Carlo simulations in neutrino physics: the\n\nexample of the SOX experiment\n\nA. Caminata1, M. Agostini2, K. Altenmüller2, S. Appel2, G. Bellini3,J. Benziger4, N. Berton5,\nD. Bick6, G. Bonfini7, D. Bravo8,B. Caccianiga3, F. Calaprice9, M. Carlini7, P. Cavalcante7,\nA. Chepurnov10, K. Choi11, M. Cribier5, D. D’Angelo3, S. Davini12, A. Derbin13, L. Di\nNoto1, I. Drachnev12, M. Durero5, A. Etenko14, S. Farinon1, V. Fischer5, K. Fomenko15,\nA. Formozov3,15, D. Franco16, F. Gabriele7, J. Gaffiot5, C. Galbiati9, C. Ghiano1, M. Giammarchi3,\nM. Goeger-Neff2, A. Goretti9, M. Gromov10, C. Hagner6, Th. Houdy5, E. Hungerford17,\nAldo Ianni7, Andrea Ianni9, N. Jonquères18, K. Jedrzejczak19, D. Jeschke2, M. Kaiser6,\nV. Kobychev20, D. Korablev15, G. Korga7, V. Kornoukhov21, D. Kryn16, T. Lachenmaier22,\nT. Lasserre5, M. Laubenstein7, B. Lehnert23, J. Link8, E. Litvinovich14,24, F. Lombardi7,\nP. Lombardi3, L. Ludhova3, G. Lukyanchenko14,24, I. Machulin14,24, S. Manecki8,W. Maneschg28,\nS. Marcocci12, J. Maricic11, G. Mention5, E. Meroni3, M. Meyer6, L. Miramonti3, M. Misiaszek7,19,\nM. Montuschi25, P. Mosteiro9, V. Muratova13, R. Musenich1, B. Neumair2, L. Oberauer2,\nM. Obolensky16, F. Ortica26, M. Pallavicini1, L. Papp2, L. Perasso1, A. Pocar27, G. Ranucci3,\nA. Razeto7, A. Re3, A. Romani26, R. Roncin7,16, N. Rossi7, S. Schönert2, L. Scola5, D. Semenov13,\nH. Simgen28, M. Skorokhvatov14,24, O. Smirnov15, A. Sotnikov15, S. Sukhotin13, Y. Suvorov14,29\n\nR. Tartaglia7, G. Testera1, J. Thurn23, M. Toropova14, C. Veyssiére5, M. Vivier5, E. Unzhakov13,\nR.B. Vogelaar8, F. von Feilitzsch2, H. Wang29, S. Weinz30, J. Winter30, M. Wojcik19, M. Wurm30,\nZ. Yokley8, O. Zaimidoroga15, S. Zavatarelli1, K. Zuber23, G. Zuzel19\n\n1Dipartimento di Fisica, Universitá degli Studi e INFN, Genova 16146, Italy\n2Physik-Department and Excellence Cluster Universe, Technische Universität München, 85748\nGarching, Germany\n3Dipartimento di Fisica, Universitá degli Studi e INFN, 20133 Milano, Italy\n4Chemical Engineering Department, Princeton University, Princeton, NJ 08544, USA\n5Commisariat à l’Énergie Atomique et aux Énergies Alternatives, Centre de Saclay, IRFU,\n91191 Gif-sur-Yvette, France\n6Institut für Experimentalphysik, Universität Hamburg, 22761 Hamburg, Germany\n7INFN Laboratori Nazionali del Gran Sasso, 67010 Assergi (AQ), Italy\n8Physics Department, Virginia Polytechnic Institute and State University, Blacksburg, VA\n24061, USA\n9Physics Department, Princeton University, Princeton, NJ 08544, USA\n10Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics, 119234 Moscow,\nRussia\n11Department of Physics and Astronomy, University of Hawai’i, Honolulu, HI 96822, USA\n12Gran Sasso Science Institute (INFN), 67100  L’Aquila, Italy\n13St. Petersburg Nuclear Physics Institute NRC Kurchatov Institute, 188350 Gatchina, Russia\n14NRC Kurchatov Institute, 123182 Moscow, Russia\n15Joint Institute for Nuclear Research, 141980 Dubna, Russia\n\nMagellan Workshop 2016 1Magellan Workshop 2016 105\n\n\n\n16AstroParticule et Cosmologie, Universit Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observa-\ntoire de Paris, Sorbonne Paris Cit, 75205 Paris Cedex 13, France\n17Department of Physics, University of Houston, Houston, TX 77204, USA\n18Commisariat à l’Énergie Atomique et aux Énergies Alternatives, Centre de Saclay,\nDEN/DM2S/SEMT/BCCR, 91191 Gif-sur-Yvette, France\n19M. Smoluchowski Institute of Physics, Jagiellonian University, 30059 Krakow, Poland\n20Kiev Institute for Nuclear Research, 06380 Kiev, Ukraine\n21Institute for Theoretical and Experimental Physics, 117218 Moscow, Russia\n22Kepler Center for Astro and Particle Physics, Universität Tübingen, 72076 Tübingen, Ger-\nmany\n23Department of Physics, Technische Universität Dresden, 01062 Dresden, Germany\n24National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409\nMoscow, Russia\n25Dipartimento di Fisica e Scienze della Terra Università degli Studi di Ferrara e INFN, Via\nSaragat 1-44122, Ferrara, Italy\n26Dipartimento di Chimica, Università e INFN, 06123 Perugia, Italy\n27Amherst Center for Fundamental Interactions and Physics Department, University of Mas-\nsachusetts, Amherst, MA 01003, USA\n28Max-Planck-Institut für Kernphysik, 69117 Heidelberg, Germany\n29Physics and Astronomy Department, University of California Los Angeles (UCLA), Los An-\ngeles, California 90095, USA\n30Institute of Physics and Excellence Cluster PRISMA, Johannes Gutenberg-Universität Mainz,\n55099 Mainz, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/21\n\nThe SOX project aims to test the existence of light sterile neutrinos. A solid signal would\nmean the discovery of the first particles beyond the Standard Electroweak Model and would\nhave profound implications in our understanding of the Universe and of fundamental parti-\ncle physics. In case of a negative result, it is able to close a long standing debate about the\nreality of the neutrino anomalies. The SOX experiment will use a 144Ce-144Pr antineutrino\ngenerator placed at short distance from the Borexino liquid scintillator detector. Particular\nemphasis is devoted in describing how a simulation of a neutrino detector is implemented\nand how it can be used to obtain useful information for the future data analysis.\n\n1 Introduction\n\nAlthough most of the collected neutrino experimental data in neutrino flux rates and their\noscillations fit well into the three-flavour oscillation model, several short-baseline neutrino ex-\nperiments have reported anomalies which significantly deviates from the three active neutrino\npictures ([1, 2, 3, 4]).\n\nAll these anomalies can be related to the presence of a forth type of neutrino with ∆m2\n14\n\nof about 1 eV 2. This new particle, not predicted from the standard model, is called sterile\nsince it is not coupled with the Z0 boson. The SOX project will exploit the unprecedented\nperformances of the Borexino detector to investigate the existence of unexpected oscillations\nin the low L/E region and therefore investigate the existence of sterile neutrinos in the ∆m2\n\n14\n\n2 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n106 Magellan Workshop 2016\n\n\n\nFigure 1: Schematic drawing of the Borexino detector.\n\nregion of ∼ 1 eV 2 [5]. The first part of the project consists in deploying a 150 kCi 144Ce−144Pr\nνe source in a dedicated pit located 8.5 m below the detector’s center.\n\n2 Antineutrino detection in Borexino\n\n2.1 The Borexino detector\n\nBorexino is an uderground liquid scintillator detector located at Laboratori Nazionali del\nGran Sasso in Italy [6]. It has an active mass of 278 tons of pseudocumene (PC, 1,2,4-\ntrimethylbenzene), doped with 1.5 g/liter of PPO (2,5-diphenyloxazole, a fluorescent dye).\nThe scintillator is contained in a thin nylon vessel (125 µm in thickness) and it is surrounded\nby two concentric PC buffers doped with 2 g/liter of DMP (dimethylphthalate), a component\nthat quenches the PC scintillation light. The two PC buffers are separated by a second thin\nnylon membrane to prevent radon diffusion toward the scintillator. The scintillator and buffers\nare contained in a stainless steel sphere (SSS) with diameter of 13.7 m. The scintillation light\nis detected by 2212 8 inch PMTs uniformly distributed in the inner surface of the SSS. The\ndetector energy and vertex reconstruction performances are respectively 5% at 1 MeV and 15\ncm. Antineutrinos are detected in Borexino by means of inverse beta decay (IBD, 1.8 MeV\nthreshold) on protons. IBD events are clearly tagged using the space-time coincidence between\nthe prompt e+ signal and the delayed neutron capture (τ = 254 µs [7]) event. Therefore, ac-\ncidental background is almost negligible. A 18.0-m diameter - 16.9-m high domed water tank\n\nMagellan Workshop 2016 3\n\nMONTE CARLO SIMULATIONS IN NEUTRINO PHYSICS: THE EXAMPLE OF THE SOX . . .\n\nMagellan Workshop 2016 107\n\n\n\n(WT) containing 2100 tons of ultrapure water surrounds the SSS providing additional shielding\nagainst external background. Moreover, additional 208 8-inch PMTs instrument the WT and\ndetect the Cherenkov light radiated by muons in the water shield, serving as muon veto. Right\nbeneath the Borexino detector, there is a cubical pit (side 105 cm) accessible through a small\nsquared tunnel (side 95 cm) that was built at the time of construction with the purpose of\nhousing possible neutrino sources. The existence of this tunnel is one of the reasons why the\n144Ce source can be deployed with no changes to the Borexino layout. The center of the pit is\nat 8.5 m from the detector center, requiring a relatively high source activity for the source.\n\n2.2 The antineutrino source\n\nThe antineutrino source must accomplish to several requirements. To get a reasonable number\nof interaction inside the active volume, an activity of about 100 kCi is required. Moreover,\nsince antineutrinos are detected via inverse beta decay (IBD), an energy spectrum that extends\nabove the IBD threshold is necessary. Furthermore, the half-life must be reasonably long to\nproduce and transport the source nearby the detector. The 144Ce source β-decays to 144Pr\n(t1/2 = 296 days) which rapidly (t1/2 = 17 min) β-decays to 144Nd emitting a spectrum of νe\n\npart of which falls above the IBD threshold (the endpoint of the 144Pr decay is about 3 MeV).\nThe 144Ce −144 Pr source has been identified as a suitable νe emitter having a long enough\nhalf-life to allow the source production and the transportation to LNGS.\n\nThe production of the source can be realized by isolating and extracting 144Ce from spent\nnuclear fuel (SNF). The source is produced as few kilograms of CeO2 containing a few tens of\ngrams of 144Ce. The source emist also gamma rays that have to be shielded both for biological\nprotection purposes and to avoid source-induced background. Consequently a cylindrical tung-\nsten alloy shielding (density greater than 18 g/cm3) surrounds the source (which is contained\nin a stainless steel capsule). The shielding, 60 cm in height and diameter, has been designed\ndriven by the 2.185 MeV gamma ray emitted with 0.7% intensity. Such dimensions ensure a\n19-cm thick shielding and a 1012 gamma-ray attenuation factor along any directions.\n\n3 Data analysis\n\nBorexino can study short distance neutrino oscillations in two ways. The first way is the\nstandard disappearance technique (rate technique): if oscillations into sterile neutrino occur, the\ntotal count rate in the detector is lower than the expected rate without oscillations. Knowing the\nsource activity (section 3.1) and measuring the interaction rate, it is possible to investigate the\nexistence of the sterile neutrino state. The second way is based on an oscillometry measurement\nwithin the detector volume (shape technique).\n\nIf the values of m2\n14 are of the order of 1eV 2 and the antineutrino energy is of the order of 1\n\nMeV, the typical oscillation length is about a few meters, being smaller that the detector typical\ndimension and greater than the spatial resolution. Consequently, the resulting oscillations waves\ncan be directly seen in Borexino and a direct measurement of m2\n\n14 and θ14 can be performed\n(figure 2). Figure 3 shows the best fit values for the current allowed phase space. Consequently,\nfrom the plot it is clear that the SOX experiment will investigate almost all the region of the\nneutrino anomaly.\n\n4 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n108 Magellan Workshop 2016\n\n\n\nFigure 2: Oscillometry: waves in the reconstructed 2D spectrum for m2\n14 = 2eV 2 and\n\nsin2(2θ14) = 0.15.\n\n3.1 Calorimetric measurement\n\nIn case of rate analysis, it is fundamental to have a precise measurement (uncertainties below\n1%) of the source activity. In order to infer the source activity, the heat released by the source\nis measured by two calorimeters (INFN-TUM and CEA). Both the calorimeters have been\nconceived to measure the source activity with high precision by knowing the power released\nby the radiation in the tungsten shield and absorbed by a water flow. In the INFN-TUM\ncalorimeter, the water flows inside a copper heat exchanger in contact with the tungsten shield\n[8], while in the CEA calorimeter, the source with the tungsten is directly immersed in the\nwater contained in the calorimeter apparatus. In both systems, neglecting the heat losses (that\nare measured by a calibration apparatus), the power is measured from the difference between\nthe water temperature entering and exiting from the calorimeter and from the water mass flow.\n\n4 Monte Carlo simulation code and the physics modelling\nthe detector response\n\nIn order to properly take into account efficiencies and signal properties, for both the rate and\nshape analyses, a deep understanding of the detector behavior is necessary. Consequently, a\nfull Monte Carlo simulation code has been developed.\n\nParticles depositing energy in the scintillator produce light (both scintillation and Cherenkov\n\nMagellan Workshop 2016 5\n\nMONTE CARLO SIMULATIONS IN NEUTRINO PHYSICS: THE EXAMPLE OF THE SOX . . .\n\nMagellan Workshop 2016 109\n\n\n\nFigure 3: Neutrino anomaly region and foreseen sensitivity for SOX.\n\neffect) which propagates inside the detector and it is detected by the 2212 PMTs. For each\nevent, it is important to relate measurable quantities (the number of photons measured and\nthe charge collected by each PMT, the time of arrival of each photon) with physical properties\nof the event(energy, position, particle identification). Consequently, a precise knowledge of the\ndetector response for different particle types and energies is important for properly connecting\nmeasurable and physical quantities. The method of evaluation of the detector response function\nis based on a Monte Carlo simulation. This code is able to predict the shapes of the signal and\nthe background both for SOX and solar neutrino analysis. The simulation takes into account\nall the processes that can influence the energy deposits inside the detector. The scintillation\nand Cherenkov light emission, the light propagation and detection processes are fully simulated.\nMoreover the read-out electronics is simulated as well. The Monte Carlo simulation output is a\nraw data file with the same format of the real data raw file, allowing an identical data processing.\nThe simulation of the whole detector, from the energy deposit up to the detection of light is\nsimulated using the standard GEANT4 [9] package. The simulation of the readout electronics\nis performed with a custom c++ code. In view of the Borexino phase II solar neutrino analysis\nand the SOX measurement, the Monte Carlo code has been extended. Particular attention\nhave been devoted in developing generators for sterile neutrino signal as well as in improving\nthe reliability of the light collection and the detector’s response for events far away from the\ncenter. The code has been widely used for solar neutrino analysis and has been tested and\nvalidated deploying calibration sources inside the active volume and comparing the simulation\n\n6 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n110 Magellan Workshop 2016\n\n\n\nresult with the acquired data (an example is reported in figure 4).\n\nFigure 4: Comparison between data and Monte Carlo: several radiactive gamma sources have\nbeen deployed in the scintillator. In black the collected charge for real events, in red for\nsimulated events. The agreement is better than 1%.\n\n5 Conclusions\n\nThe Borexino detector is an ideal tool to search for sterile neutrinos. It is possible to test the\nexistence of sterile neutrinos using an anti-neutrino source with almost negligible background.\nA deep knowledge of the detector response is fundamental for a proper interpretation of the\nacquired data. Consequently a Borexino Monte Carlo simulation code has been developed. In\nthe last years the code has been refined and extended to take into account the SOX needs.\nCalibration campaigns with radioactive sources have been performed over the years. A new\ncalibration is foreseen for the end of this year allowing a final tuning of the code before the\nstart of the data-taking expected for the first part of 2017.\n\nReferences\n[1] Aguilar A et al. 2001 Phys. Rev. D 64 112007\n\n[2] Aguilar A et al. 2013 Phys. Rev. Lett. 110 161801\n\n[3] Mention G et al. 2011 Phys. Rev. D 83 073006\n\n[4] Giunti C and Laveder M 2011 Phys. Rev. C 83 065504\n\n[5] Bellini G et al. 2013 JHEP 38 1308\n\n[6] Bellini G et al. 2014 Phys. Rev. D 89 112007\n\n[7] Agostini M et al. 2015 Phys. Rev. D 92 031101\n\nMagellan Workshop 2016 7\n\nMONTE CARLO SIMULATIONS IN NEUTRINO PHYSICS: THE EXAMPLE OF THE SOX . . .\n\nMagellan Workshop 2016 111\n\n\n\n[8] Farninon S et al. 2016 INFN technical note INFN-16-08/GE\n\n[9] Geant4 Collaboration, Physics Reference Manual\n\n8 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n112 Magellan Workshop 2016\n\n\n\nSolar neutrino detection in a large volume double-\n\nphase liquid argon experiment\n\nDavide Franco\n\nAPC, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris\nCitè, Paris 75205, France\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/14\n\nTwo-phase liquid argon time projection chambers (LAr TPCs) are prime candidates for the\nambitious program to explore the nature of dark matter. The large target, high scintillation\nlight yield and good spatial resolution in all three cartesian directions concurrently allows\nalso a high precision measurement of solar neutrino fluxes via elastic scattering. We studied\nthe cosmogenic and radiogenic backgrounds affecting solar neutrino detection in a 100 tonne\nfiducial LAr TPC operating at LNGS depth. Such a detector has the potential to measure\nthe CNO neutrino rate with 5 σ sensitivity, and to significantly improve the precision on\nthe measured 7Be and pep neutrino fluxes.\n\n1 Introduction\n\nThe study of solar neutrinos has given a fundamental contribution to both astroparticle and\nelementary particle physics, offering an ideal test of solar models and, at the same time, rel-\nevant indications on the fundamental interactions among particles. Radiochemical (Homes-\ntake [1], GALLEX/GNO [2, 3], SAGE [4]) and real-time experiments (Kamiokande [5], Su-\nperKamiokande [6], SNO [7] and Borexino [8]) have already measured the solar neutrino com-\nponents from the 7Be, 8B, pep, and pp reactions, all belonging to the dominant mechanism for\nthe energy production in the Sun: the proton-proton chain. At the same time, no neutrino from\nthe 13N, 15O, and 17F reactions, from the CNO cycle, has been yet observed. The CNO cycle\nhas a key role in astrophysics, since it is the dominant source of energy in stars more massive\nthan the Sun and in advanced evolutionary stages of solar-like stars. In addition, constraining\nthe chemical composition of the Sun will improve astrophysical models like for star formation\nand Super Nova explosion.\n\nCNO neutrinos may also solve the long-standing and well–known solar metallicity problem.\nThe abundance of the heavy elements, Z (aka metals) in the Sun is a critical parameter in the\nSSM, since it affects the radiative opacity, and the boundary definition of the convective zone.\nIn particular, abundances of oxygen, carbon, and nitrogen have a direct impact on the energy\nproduction of the Sun via the CNO cycle. The SSM predicts different neutrino fluxes if assuming\neither high–Z [9] or low–Z [10] chemical compositions in the Sun. The largest discrepancy in\nthe prediction of solar neutrino fluxes resides in the CNO component: a measurement of the\nCNO neutrino flux at 10-20% accuracy has the potential to solve the metallicity problem.\n\nThe most stringent experimental limit on the CNO neutrino rate is from Borexino [8] using\nelastic neutrino-electron scattering. They obtained an upper limit for the interaction rate of\n\nMagellan Workshop 2016 1Magellan Workshop 2016 113\n\n\n\nNeutrino Source\nLow Metallicity (LZ) High Metallicity (HZ)\nAll [0.6-1.3] MeV All [0.6-1.3] MeV\n\npp 107.9 ± 2.0 0 107.0 ± 2.0 0\npep 2.28 ± 0.05 1.10 ± 0.02 2.23 ± 0.05 1.07 ± 0.02\n7Be 36.10 ± 2.60 2.85 ± 0.21 39.58 ± 2.85 3.13 ± 0.23\nCNO 3.06 ± 0.30 0.64 ± 0.06 4.28 ± 0.44 0.90 ± 0.09\n8B 0.30 ± 0.04 0.035 ± 0.005 0.36 ± 0.06 0.042 ± 0.007\nTotal 4.63 ± 0.22 5.14 ± 0.25\n\nTable 1: Expected solar neutrino rates in cpd/100 tonne of LAr active mass, comparing the\nlow-metallicity [11] and high-metallicity [12] predictions using the Standard Solar Model and\nneutrino oscillation parameters from the MSW-LMA [13] region with ∆m2 = 7.54×10−5 eV2\n\nand sin2(θ12) = 0.307.\n\n7.9 counts per day per 100 tonnes (cpd/100 tonne) at the 95% C.L., which corresponds to a\nflux limit of <7.9×106 cm−2 s−1 [8]. Despite the contamination from 210Bi in the active mass\nof Borexino is exceptionally low (∼20 cpd/100 tonne), its spectral shape is very similar to the\nexpected CNO signal, precluding a positive observation of CNO neutrinos.\nLiquid argon (LAr) may represent the ideal target for measuring the CNO neutrino component\nfrom the Sun. LAr, in fact, is a powerful scintillator, 4-5 times brighter than organic liquid\nscintillators and, as a liquefied noble element, does not react and does not bond with chemical\nspecies. In addition, the use of a two-phase time projection chamber (TPC) technique can\nfurther abate the background. In a two-phase TPC, a thin gas layer above the LAr target\nvolume converts the ionization electrons, once extracted and accelerated by an electric field, in\na secondary light signal by gas proportional scintillation. LAr TPC can determine the position\nof energy deposit events in the liquid to within a few mm in the drift direction, and ∼1 cm\nor better in the two transverse directions, removing backgrounds from surfaces with a sharp\ndefinition of a fiducial volume. Moreover, the identification of multiple-Compton scatter events\nand other events having multiple energy deposition sites.\nThe overall solar neutrino-electron scattering rate expected in LAr is ∼150 cpd/100 tonne,\nas shown in table 1. However, the spectral range below 0.6 MeV is inaccessible due to the\nintrinsic contamination of 39Ar, a β–decay (Q = 0.565 MeV) produced by cosmic ray spallation\nof natAr. Argon extracted from underground gas wells (UAr) has been shown by the Darkside\ncollaboration to contain only ∼0.7 mBq/kg of 39Ar [14]. But even this level would prevent\nextraction of solar neutrino signals in the low energy region, which is dominated by pp neutrinos.\nHowever, 7Be neutrino interactions, whose Compton-like edge is expected at ∼0.66 MeV, would\nbe accessible thanks to the excellent energy resolution achievable in LAr.\nAssuming a light yield of 6,000 photoelectrons / MeV, as measured by MicroCLEAN [15],\nthe number of expected CNO neutrinos, interacting via elastic scattering in 400 tonne×year\nexposure of LAr and above 0.6 MeV, is between ∼900 and 1,300 events, depending on the\nmetallicity model, as shown in figure 1. The main limitation to their detection is the background\nfrom in situ produced cosmogenic isotopes, radon emanated from the detector inner walls and\ndistributed throughout the purification/recirculation loop, and from the external background.\nIn the following sections, we will review the impact of each contaminant on the solar neutrino\ndetection with a 100 ton fiducial mass two-phase LAr TPC .\n\n2 Magellan Workshop 2016\n\nDAVIDE FRANCO\n\n114 Magellan Workshop 2016\n\n\n\nEnergy [MeV]\n1−10×6 1−10×7 1−10×8 1\n\n \ny\nr\n]\n\n×\n \n4\n0\n0\n \nt\no\nn\nn\ne\n \n\n×\nE\nv\ne\nn\nt\ns\n \n/\n \n[\n1\n0\n \nk\ne\nV\n \n\n1−10\n\n1\n\n10\n\n210\n\n310\n\n410\n\n510\n\n610\nAr\n\n39\n\n (HZ)νAll \n\n (LZ)νAll \n\n (HZ)νBe \n7\n\n (LZ)νBe \n7\n\n (HZ)νCNO \n\n (LZ)νCNO \n\n (HZ)νpep \n\n (LZ)νpep \n\n (HZ)νB \n8\n\n (LZ)νB \n8\n\nFigure 1: Simulated solar neutrino spectra in a 400 tonne-yr LAr TPC exposure, assuming\nσ =1.3% energy resolution at 1 MeV, corresponding to a PE yield of 6 PE/keV. The blue\nshaded area represents the tail of the 39Ar contamination, intrinsic to underground LAr.\n\n2 The background\n\nThe detector is assumed to be located at the Gran Sasso Laboratory, shielded by 1,400 m\nof rock. Despite the deep underground location, the residual muon flux may still produce\nmeasurable amounts of radioactive isotopes by muon–induced spallation on argon. These can\nproduce dangerous, delayed electron-recoil background in the solar neutrino energy window.\nThe cosmogenic radionuclide production was simulated with FLUKA assuming a cylindrical\nTPC of 3.3 m radius and 3 m height. The TPC sidewall is a 3 cm thick teflon layer, and the top\nand bottom ends are covered with 2 mm thick silicon layers, representing silicon photomultiplier\narrays. The 2 cm thick gaseous argon region sits just below the upper silicon layer. The TPC is\ncontained in a 3 mm thick cylindrical stainless steel cryostat with 3.5 m radius and 3.2 m height.\nGaseous argon is also present in the cryostat, outside the TPC region and above the LAr level.\nThe cryostat is housed in a Borexino-like veto detector consisting of a 6 m radius stainless steel\nsphere filled with liquid scintillator placed within a larger cylindrical tank (17 m height, 16 m\ndiamater) filled with water. Cosmic muons were generated 3 m above the cylindrical water\ntank, crossing 0.7 m rock layer, in order to fully develop the hadronic showers.\n\nThe simulation resulted in the production of more than 80 isotopes by muon spallation\non argon, each of them handled by a GEANT4-based simulation, which generated decays and\ntracked the products in the full detector geometry. This allowed to estimate the efficiency of\nthe multiple scattering cut. The predicted single scatter background induced by cosmogenic\nisotopes is evaluated in 0.733 cpd/100 tonne a factor ∼7 lower than the overall neutrino signal\nin the energy region of interest between 0.7 and 1.3 MeV. The dominant contribution comes\nfrom 32P, 38Cl, and 32P, with a decay rate of 0.332, 0.147, and 0.106 cpd/100 tonne respectively\n[16].\n\nThe external background contribution is due to radioactive contaminants in the detector\nmaterials. The largest contributions are expected to have origin in the photosensors and in\nthe cryostat. Gamma-rays are the only events able to reach the active mass and to produce\n\nMagellan Workshop 2016 3\n\nSOLAR NEUTRINO DETECTION IN A LARGE VOLUME DOUBLE-PHASE LIQUID ARGON . . .\n\nMagellan Workshop 2016 115\n\n\n\na signal that can mimic a neutrino event. The strategy to abate this class of background is\nprimarily based on the multiple scattering cut and on the fiducialization of the active volume.\nWe estimated that the attenuation lengths for γ’s induced by 40K, 214Bi, and 208Tl originating\nin the photosensors are of the order of ∼4 cm. As a consequence, a 30 cm cut from the TPC\nwalls in combination with the multiple scattering cut, would reduce such contamination by a\nfactor ∼106.\nIn the present work we assume 2 mm thick silicon photomultipliers (SiPM) as photosensors, on\nthe top and bottom of the TPC, corresponding to ∼300 kg of silicon. A recent measurement by\nI. Ostrovskiy et al. [17] quoted an overall contamination in SiPM from 40K, and the 238U and\n232Th smaller than 0.1 mBq/kg. In the here–proposed detector, this activity corresponds to an\nupper limit of 30 mBq. After the selection cuts, the external background contribution from the\nSiPM’s of ∼2.6×10−3 cpd/100 tonne, negligible with respect to the solar neutrino rate.\nThe dominant contribution for the external background is expected coming from the cryostat,\nwhose mass is of the order of the tonne–scale. Stainless steel cryostats are contaminated in 60Co\nat the level of tens of mBq/kg. The correspondent activity in the LAr target was estimated\nto be of the same order of the solar neutrino interaction rate in the energy window of interest,\nassuming a fiducial volume of 30 cm from the TPC walls. The 60Co issue could be solved by\neither applying stronger fiducial volume cuts or by making the cryostat of titanium, which is\nalmost free of 60Co [18]. Further, an external active veto as in DarkSide-50 [19] would further\nreduce the 60Co background by vetoing events with 60Co gammas detected in coincidence by\nthe TPC and the veto. In the sensitivity study described in this work, we then assume negligible\nthe background from external sources.\n\nContamination of the target argon mass by 222Rn represents the most critical background\nfor the solar neutrino measurements. 222Rn can be emanated into the active argon from the\nTPC materials in contact with the LAr target and/or distributed throughout the LAr fill by\nthe recirculation loop. The main goal of this work is to evaluate the maximum contamination\nof 222Rn not preventing the CNO neutrino measurement. In particular, only the 214Pb and\n214Bi isotopes of the 222Rn decay segment can provide a signal that could mimic the neutrino\none. In fact, α’s can be efficiently discriminated by the use of the LAr scintillation pulse\nshape discrimination, which has a rejection power at the 107–108 level [20]. β–decays with\nsimultaneous emission of γ-rays produce multi-site energy deposits, that can be identified and\nrejected by means of the multiple scattering cut. The simulations indicate that only 6.9% (5.9%)\nof 214Pb (214Bi) decays will produce a single site deposit in the solar neutrino energy region.\nHowever, despite most of the 222Rn induced events can be rejected, the residual contamination\nrepresents a serious background: we estimated that 45 µBq/(100 tonne) of 222Rn contamination\nwould introduce a background rate equivalent to the expected solar neutrino signal. The overall\nspectrum, including all the contaminations, is shown in figure 2.\n\n3 Sensitivity study\n\nThe measurement of the rate of each solar neutrino component relies on the identification of\nthe individual spectral shapes. To study the sensitivity of a large LAr TPC we exploited a\ntoy Monte Carlo approach. The data sample was produced by generating the neutrino signal\nand the background components discussed in the previous section, assuming a 400 tonne×yr\nstatistics. The data sample was generated for each metallicity model and for a given radon\n\n4 Magellan Workshop 2016\n\nDAVIDE FRANCO\n\n116 Magellan Workshop 2016\n\n\n\nEnergy [MeV]\n\n1−10×4 1 2 3 4 5 6\n\n \ny\nr\n]\n\n×\n \n4\n0\n0\n \nt\no\nn\nn\ne\n \n\n×\nE\nv\ne\nn\nt\ns\n \n/\n \n[\n1\n0\n \nk\ne\nV\n \n\n2−10\n\n1−10\n\n1\n\n10\n\n210\n\n310\n\n410\n\n510\n\n610\n\n710\n\n810\n\n910 All\n\nνSolar  \n\nAr39\n\nCosmogenics\n\nBq/(100 tonne)µRn: 100 222\n\nFigure 2: Components of the electron recoil energy spectrum, simulated for a 4 year exposure\nof 100 tonnes of LAr (see legend), assuming a radon contamination in LAr of 100 µBq/(100\ntonne) .\n\ncontamination, and then fitted with an analytical model. The procedure was repeated 10,000\ntimes for each set of parameters. An example of fit is shown in figure 3\n\nThe cosmogenic spectrum was observed to be rather flat after the multiple scattering cut,\nwith the exception of the contribution from 32P, as shown in figure 3. Cosmogenics have been\nthen modeled with a linear energy function plus the beta shape from 32P. The systematics\nassociated to this model choice was deeply investigated and, as shown in reference [16], can be\nassumed negligible.\nThe CNO spectral shape is similar to that of the low energy radon component. At radon\ncontamination levels above 200 µBq/(100 tonne), the fit shows a systematic deviation from the\ncentral value of the simulated CNO component (SSM-LZ) by a few percent, implying that to\nguarantee a correct CNO measurement, the radon activity must be reduced below this level.\nNo systematic deviations were observed for 7Be and pep, whose spectral shapes have clear\ncharacteristic features.\n\n4 Conclusions\n\nThe final results are summarized in figure 4. Assuming to be able to contain the radon contami-\nnation below 200 µBq/(100 tonne), a LAr TPC with an exposure of 400 tonne×years exposure,\nhas the power to observe (>5 σ) CNO neutrinos and to measure 7Be and pep neutrinos with\na statistical accuracy of ∼2% and ∼10%, respectively. At the same time, the associated sys-\ntematics connected to the definition of the fiducial volume and to the determination of the\nenergy scale were evaluated to be less than 1%, thanks to the exception resolution in the posi-\ntion reconstruction and to the light yield, more than 10 times larger than in an organic liquid\nscintillator (more details can be found here: [16]).\nIn conclusion, a large volume LAr TPC, designed for direct dark matter WIMP search, has also\nthe potential to experimentally observe the CNO neutrinos from the Sun, and, more in general,\nto provide a rich set of physics results for solar neutrinos.\n\nMagellan Workshop 2016 5\n\nSOLAR NEUTRINO DETECTION IN A LARGE VOLUME DOUBLE-PHASE LIQUID ARGON . . .\n\nMagellan Workshop 2016 117\n\n\n\nEnergy [MeV]\n1−10×6 1 2 3 4\n\n \ny\nr\n]\n\n×\n \n4\n0\n0\n \nt\no\nn\nn\ne\n \n\n×\nE\nv\ne\nn\nt\ns\n \n/\n \n[\n1\n0\n \nk\ne\nV\n \n\n1\n\n10\n\n210\n\n310\nMC Data\n\nAll\n\nνBe-7\n\nνCNO-\n\nνpep-\n\nνB-\n8\n\nP\n32\n\nRadon\n\nOther Cosmogenics\n\nFigure 3: Example of simulated spectra and fit, assuming radon contaminations of 100 µBq/(100\ntonne), assuming the low–metallicity SSM The cosmogenic component is modeled with a first\ndegree polynomial, with the exception of an explicit spectrum for 32P.\n\nBq / 100 tonne]µRadon Activity [ \n10 20 30 40 50 60 70 80 210 210×2\n\nU\nn\nc\ne\nr\nt\na\ni\nn\nt\ny\n \n \n[\n%\n]\n\n0\n\n5\n\n10\n\n15\n\n20\n\n25\n\n30\nCNO (HZ)\n\npep (HZ)\n\nBe (HZ)\n7\n\nCNO (LZ)\n\npep (LZ)\n\nBe (LZ)\n7\n\nFigure 4: Statistical uncertainties on the solar neutrino components, as a function of the radon\nactivity.\n\n6 Magellan Workshop 2016\n\nDAVIDE FRANCO\n\n118 Magellan Workshop 2016\n\n\n\n5 Acknowledgments\n\nWe acknowledge the financial support from the UnivEarthS Labex program of Sorbonne Paris\nCité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02).\n\nReferences\n[1] R. Davis, D. S. Harmer, K. C. Hoffman, Phys. Rev. Lett. 20 (1968) 1205.\n\n[2] W. Hampel et al. (GALLEX Collaboration), Phys. Lett. B420 (1998) 114.\n\n[3] M. Altmann et al. (GNO Collaboration), Phys. Lett. B. 490, 16 (2000).\n\n[4] J.N. Abdurashitov et al. (SAGE collaboration), Phys. Rev. C 80, 015807 (2009).\n\n[5] Y. Fukuda et al. (Kamiokande Collaboration), Phys. Rev. Lett. 77 (1996) 1683\n\n[6] Y. Fukuda et al. (Super-Kamiokande Collaboration), Phys. Rev. Lett. 81, 1562 (1998).\n\n[7] B. Aharmim et al. (SNO Collaboration), Phys. Rev. C 72, 055502 (2005).\n\n[8] G. Bellini et al. (Borexino Collaboration), Phys. Rev. D 89 112007 (2014).\n\n[9] N. Grevesse and J. Sauval, Space Science Reviews 85 (1998) 161.\n\n[10] M. Asplund et al., Ann. Rev. Astr. & Astrophys. 47 (2009) 481.\n\n[11] M. Asplund et al., Ann. Rev. Astron. Astroph. 47 (2009) 481.\n\n[12] N. Grevesse, A. J. Sauval, Spece Sci. Rev. 85 (1998) 161.\n\n[13] S. P. Mikheyev and A. Yu. Smirnov, Sov. J. Nucl. Phys. 42 (1985) 913; L. Wolfenstein, Phys. Rev. D 17\n(1978) 2369.\n\n[14] P. Agnes et al. (DarkSide Collaboration) Phys. Rev. D 93 (2016) 081101.\n\n[15] W. H. Lippincott et al., Phys. Rev. C 81 (2010) 045803.\n\n[16] D. Franco et al., arXiv:1510.04196 (2015).\n\n[17] I. Ostrovskiy et al., IEEE T. Nucl. Sci. 62 (2015) 1825.\n\n[18] D. S. Akerib et al. (LZ Collaboration) arXiv:1509.02910 (2015).\n\n[19] P. Agnes et al. (DarkSide Collaboration) JINST 11 03 (2016) 03016.\n\n[20] P. Agnes et al. (DarkSide Collaboration) Phys. Lett. B 743 (2015) 456.\n\nMagellan Workshop 2016 7\n\nSOLAR NEUTRINO DETECTION IN A LARGE VOLUME DOUBLE-PHASE LIQUID ARGON . . .\n\nMagellan Workshop 2016 119\n\n\n\n\n\nNeutrino Astrophysics with JUNO\n\nSebastian Lorenz1 for the JUNO collaboration\n\n1Johannes Gutenberg-Universität Mainz – Institut für Physik\nStaudingerweg 7, 55128 Mainz, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/20\n\nThe Jiangmen Underground Neutrino Observatory will aim for the determination of the\nneutrino mass hierarchy with a medium baseline reactor νe oscillation experiment. With\nits 20 kt liquid scintillator detector, it will also be sensitive to astrophysical low-energy\nneutrinos. In case of a typical galactic core-collapse supernova, ∼ 5000 neutrino interac-\ntions will be detected via the inverse beta decay alone. Moreover, a 3σ evidence for the\ndiffuse supernova neutrino background seems within reach after ten years of measurement.\nThe performance of the experiment’s solar neutrino program will decisively depend on the\nachieved radiopurity and cosmogenic background rejection efficiency.\n\nAs underlined by the ongoing measurements of solar neutrinos by Borexino [1], the search\nfor the diffuse supernova neutrino background (DSNB) with Super-Kamiokande [2] or the\ngeneral waiting for the next core-collapse supernova (SN) after SN1987A [3] that provides\na neutrino signal, neutrino astrophysics is a vivid, interdisciplinary field in neutrino physics.\nMeasurements of astrophysical low-energy neutrinos by finished and still running experiments\nalready provided valuable information on the light neutral leptons and their extraterrestrial\nsources. However, the answering of currently outstanding questions concerning SN neutrinos,\nthe DSNB or νe’s from the Sun requires neutrino observatories of the next generation. The\nJiangmen Underground Neutrino Observatory (JUNO) [4, 5], although under construction for\na different primary purpose, prepares to aid with their answering.\n\nA brief overview of the main goal of JUNO and the design of its 20 kt liquid scintillator\ndetector is subject of Section 1. The use of the detector to collect a high-statistics neutrino\nsample with energy, time and partial flavor information in case of a galactic core-collapse SN is\ntopic of Section 2. Besides the opportunity to learn more about the properties of neutrinos, such\na sample of SN-ν events will be a critical test for models of stellar collapses. The estimated\nperformance of JUNO to detect the predicted flux of low-energy neutrinos from past core-\ncollapse SNe, the DSNB, is covered in Section 3. Finally, Section 4 outlines a possible solar\nneutrino program of JUNO.\n\n1 The JUNO project\n\nThe JUNO project is an international undertaking centered on the construction of a 20kt liquid\nscintillator neutrino detector in the south of China. Its primary goal is the determination of the\nneutrino mass hierarchy (MH). This means to answer the question whether the third neutrino\nmass eigenvalue m3 is larger (∆m2\n\n31 and ∆m2\n32 > 0; ∆m2\n\nij = m2\ni −m2\n\nj) or smaller (∆m2\n31 and\n\nMagellan Workshop 2016 1Magellan Workshop 2016 121\n\n\n\nFigure 1: Design of the 20 kt liquid scintillator detector of JUNO. For details see [4, 5].\n\n∆m2\n32 < 0) than the other two mass eigenvalues m1 and m2, which are known to be separated\n\nby ∆m2\n21 > 0. To do so, a precise measurement of the hierarchy-dependent oscillatory fine\n\nstructure in the energy-dependent survival probability of reactor νe’s will be performed. Two\nnuclear reactor complexes with about 53km distance to the detector will provide the νe flux of\ninterest. The measurement procedure requires an energy resolution of 3% at 1MeV in order to\ndiscriminate the different hierarchy signatures (for details see [4]).\n\nAs usual for hydrocarbon-based detectors, the primary detection channel for the νe’s is\nthe inverse beta decay (IBD) on free protons: νe + p → e+ + n. The signature from the\ncoincidence of the prompt positron annihilation (on the order of nanoseconds) and the delayed\nneutron capture (after about 200µs) allows for an efficient background rejection. With about\n100k IBD events, the expected median MH sensitivity of JUNO alone is around 3σ. This\ncorresponds to six years of running with ten reactor cores and a total thermal reactor power\nof 36GW.1 Taking into account external information with 1.5-1% precision on the modulus of\nthe atmospheric mass splitting ∆m2\n\nµµ (a linear combination of ∆m2\n21, ∆m2\n\n31 and ∆m2\n32), the\n\nmedian sensitivity reaches 3.7-4.4σ [4]. Additionally, the high-statistics reactor νe measurement\nwill reduce the uncertainty on the underlying (effective) flavor oscillation parameters sin2 θ12,\n∆m2\n\n21 and |∆m2\nee| to sub-percent level [4].\n\nThe site for the new neutrino observatory is located close to Kaiping, about 150 km west\nof Hong Kong and about 120km south-west from Guangzhou. With JUNO being a funded\nproject – and aiming for a start of data taking by 2020 – the first construction works for the\nunderground laboratory started at the beginning of 2015. It will have a rock overburden of\nabout 700m (∼ 1800mw.e.), leaving an atmospheric muon event rate on the order of 3 s−1 in\nthe detector.\n\nBoth the unprecedented liquid scintillator mass of 20 kt and the design requirements to reach\nthe energy resolution goal will make JUNO advance the liquid scintillator technology beyond the\n\n1The total thermal reactor power at the start of data taking by 2020 will be 26.6GW.\n\n2 Magellan Workshop 2016\n\nSEBASTIAN LORENZ\n\n122 Magellan Workshop 2016\n\n\n\ncurrent state of the art. In the existing detector design, which is illustrated in Figure 1, the core\ncomponent of the detector is the LAB-based liquid scintillator as designated neutrino target. It\nis contained in an acrylic sphere of 35.4m inner diameter. Surrounded by ultra-pure water, the\nsphere is hold in place by a stainless-steel latticed shell with 40.1m inner diameter. The shell\nalso holds the inward-facing photosensors. About 17,000 photomultiplier tubes (PMTs) with 20 ”\ndiameter constitute the base system for the calorimetric energy measurement. Placed between\nthe large PMTs, around 34,000 additional PMTs with 3 ” diameter provide a second calorimetry\nsystem and better timing for event position reconstruction. To reach the required energy\nresolution in terms of photon–electron statistics, the design goal for the photoelectron (pe) yield\nis about 1200 pe/MeV after non-uniformity corrections. This implies certain requirements for\nthe PMT photocathode coverage (≥ 75%), the maximum PMT quantum efficiency (≥ 35%) and\nthe liquid scintillator properties (attenuation length ≥ 20m at 430 nm wavelength). Moreover,\nthe energy resolution is also connected to a precise calibration of the energy scale, which must\nbe ensured in the presence of dark noise from the PMTs and the electronics as well as finite\nvertex and PMT charge resolutions.\n\nThe described detector construct is submerged and anchored in a cylindrical water pool of\n43.5m height and diameter. Equipped with about 2,000 PMTs of 20” diameter, the ultra-pure\nwater in the pool acts as Cherenkov-based atmospheric muon veto and as shielding against\nexternal radiation. A tracker composed of plastic scintillator strips for downward-going muons\npartially covers the pool on the top.\n\n2 Neutrinos from a core-collapse supernova burst\n\nThe death of a massive star in a core-collapse SN is among the most violent phenomena in the\nvisible universe. Most of the energy released in a SN burst, about 3× 1053 erg, is carried away\nby myriads of neutrinos and antineutrinos of all flavors. They are produced at the origin of\nthe final explosion, making the weakly interacting particles valuable messengers for information\ndirectly from the star’s collapsing interior. The information they provide is complementary to\nthe one from the optical SN signal and can be gathered even if the SN’s visual appearance is\nobscured by galactic dust.\n\nA flavor, time and energy resolved neutrino burst signal from a core-collapse SN would\nallow to test our current understanding of the dynamics inside a dying star (e.g., see [6]). In\nparticular, theoretical expectations regarding different stages of the cosmic event, like the νe\nburst, the accretion phase and the cooling phase, or effects from (collective) neutrino flavor\noscillations could be probed. Both gravitational wave data and an abruptly ending SN-ν signal\ncould even indicate the formation of a black hole. As a consequence, the broad insights into\ncore-collapse physics offered by SN-ν’s sets the collection of a high-statistics sample of these\npromising witnesses among the top priorities of neutrino astrophysics. Once operational, JUNO\nwill stand by for the next of only 1-3 expected low-energy neutrino bursts per century from a\ngalactic core-collapse SN.\n\nReal-time detection of SN-ν’s with the liquid scintillator detector of JUNO will benefit from\nthe very good energy resolution, the large target mass and partial flavor information. As listed\nin Table 10 of [4] for different SN-ν mean energies and a galactic core-collapse SN at a typical\ndistance of 10 kpc,2 between 4300 and 5700 SN-νe events are expected in the IBD channel alone\nduring the about 10 s lasting SN signal. However, the hydrocarbon-based target material offers\n\n2Numbers are without flavor conversion effects.\n\nMagellan Workshop 2016 3\n\nNEUTRINO ASTROPHYSICS WITH JUNO\n\nMagellan Workshop 2016 123\n\n\n\nFigure 2: Left: Core-collapse SN probability (lower panel) based on different models and peak\nIBD event rate (upper panel) as a function of the distance to the SN. The inlay in the upper\npanel shows the expected peak IBD event rate specifically for Betelgeuse at 0.197±0.045kpc [8].\nRight: Expected neutrino event rate in JUNO from the silicon burning stage of a massive 20M⊙\nstar at 0.2 kpc distance as a function of the time before the SN explosion. The drop in the event\nrate after the silicon burned out gives information on the progenitor mass. Figures from [4].\n\nadditional opportunities for time-resolved detections of neutrino interactions: Charged current\n(CC) reactions on carbon nuclei, νe +\n\n12C → e+ + 12B and νe +\n12C → e− + 12N, probe the νe\n\nflux, but additionally give access to the νe component. One neutral current (NC) interaction\non carbon, ν (ν) + 12C → ν (ν) + 12C∗, and elastic scattering on electrons are sensitive to the\ntotal neutrino flux. Even more NC events are expected to occur via elastic neutrino scattering\non free protons [7]. These interactions are detectable in liquid scintillator, but require that\nthe heavily quenched visible energy from the proton recoil is above the low-energy limit at\nabout 200 keV, which is imposed by background from intrinsic 14C decays. With 600 to 2000\nexpected events above this threshold, the elastic neutrino–proton scattering channel would have\nthe second highest statistics.\n\nJUNO will be sensitive mostly to galactic SNe. The large number of events in JUNO at\nsuch occasions provides detailed neutrino “light curve” spectra and allows to test core-collapse\nSN models. Due to the high event rate during the 10 s SN-ν signal, background is no serious\nconcern. For SNe at larger distances R, the number of neutrino events decreases as 1/R2.\nAs an example, only about 200 events are expected from the 50 kpc distant Large Magellanic\nCloud where SN 1987A [3] exploded and produced the first and last measured SN-ν signal. The\nobjective not to miss the next galactic core-collapse SN also means that the JUNO detector\nmust not be “blinded” by neutrinos in case of a nearby incident. A particular close SN candidate\nis the around 0.197kpc distant red supergiant Betelgeuse [8] with about 18 solar masses. If it\nbecomes a SN, the expected peak IBD rate in JUNO reaches about 108 s−1 (see upper panel\nof Figure 2 left) for some tens of milliseconds roughly during the first 200ms of the SN signal.\nThis sets additional constrains for the design of the data acquisition.\n\nDue to the capabilities of JUNO, the detector will surely become part of the Supernova\nEarly Warning System (SNEWS) [9]. For close progenitors, e.g., Betelgeuse, the ultimate pre-\n\n4 Magellan Workshop 2016\n\nSEBASTIAN LORENZ\n\n124 Magellan Workshop 2016\n\n\n\nwarning system is the detection of neutrinos from the short silicon-burning stage briefly before\nthe explosion, as illustrated in Figure 2 right. Besides the use of such pre-SN neutrinos to\nverify stellar models, the sudden drop of the event rate when the silicon in the star burned out\ncontains information on the progenitor mass.\n\nNeutrinos from SNe can be used to locate the cosmic incident even if the visual appearance\nis obscured. One method relies on the study of the displacement between the IBD vertices from\nthe prompt positron signal and the delayed neutron capture, which is statistically related to\nthe original neutrino direction [10]. Assuming 5000 IBD events in JUNO, it is expected that\nthe sky coordinates of the galactic core-collapse SN can be measured with about 9◦ uncertainty,\nperhaps even better.\n\n3 Neutrinos from the diffuse supernova neutrino\nbackground\n\nThe DSNB is the flux of neutrinos from past core-collapse SNe in the visible universe. Its\nmeasurement would provide valuable information on the star formation rate, the average core-\ncollapse neutrino spectrum and the rate of failed SNe. So far, only upper limits on the flux\ncould be set by Super-Kamiokande [2].\n\nThe DSNB sensitivity forecast for JUNO adopted the practice for the physics study of the\nLow Energy Neutrino Astronomy (LENA) project [11]. It relies on a parametric representation\nof the isotropic DSNB flux, where important ingredients are the SN rate as a function of\nred shift and the neutrino flux spectrum (for details see [4]). The latter was taken to be a\nMaxwell-Boltzmann distribution, which introduced the average neutrino energy as parameter\nwith considered values between 12 and 21MeV. Taking flavor conversion effects into account,\nit is assumed that the total energy release in neutrinos per core-collapse SN is distributed\nevenly between neutrinos and antineutrinos of all flavors. Therefore, the IBD channel is used\nto measure the νe component of the DSNB flux with the liquid scintillator detector of JUNO.\n\nAbout 0.9 to 1.7 DSNB events per year are expected for JUNO in the analysis energy win-\ndow from 11 to 30MeV after all cuts, depending on the average νe energy. Below the lower limit\nof the visible energy window, the strong, irreducible background from reactor νe dominates.\nAt energies above the upper limit, CC interactions of atmospheric νe are the dominant back-\nground. Moreover, the NC interactions of atmospheric neutrinos above 8MeV can fake an IBD\nsignal: If a neutron knocked-out from carbon scatters on protons, a prompt signal signature\nis created. The neutron capture subsequently makes the delayed signal. However, pulse shape\ndiscrimination (PSD) for the light3 signal of the prompt event and the search for a disexcitation\nof a left-behind 11C nucleus can help to reduce this challenging background inside the energy\nrange of interest. In fact, the PSD performance is decisive for the sensitivity to the DSNB. As\nfor LENA, a NC background rejection efficiency of 98.9% at the cost of 50% of the signal is\nassumed. Due to the PSD and the energy cuts, backgrounds from cosmogenic isotopes and fast\nneutrons can be neglected. Table 13 in [4] lists expected DSNB detection significances after ten\nyears of measurement with JUNO for different assumptions about the average neutrino energy\nand systematic background uncertainties. For favored DSNB parameters, a 3σ evidence for\nthe DSNB signal seems within reach. As shown in Figure 3, a significant improvement of the\n\n3The observed scintillation pulse shape of the liquid scintillator is different for traversing positrons and\nprotons.\n\nMagellan Workshop 2016 5\n\nNEUTRINO ASTROPHYSICS WITH JUNO\n\nMagellan Workshop 2016 125\n\n\n\nFigure 3: Predicted DSNB exclusion contour (90%C.L.; lower red line) if JUNO finds no DSNB\nsignal above background after ten years of measurement. The upper limit for the DSNB flux\nnormalization Φ0 (for details see [4]) is given as a function of the mean energy of the SN-\nνe spectrum 〈Eνe\n\n〉. For comparison, the upper limits from the Super-Kamiokande results\n(upper blue line) presented in [2] are also shown. Figure from [4].\n\ncurrent upper limits on the DSNB flux from Super-Kamiokande [2] can be made if there is\nno positive detection after ten years.4\n\n4 Neutrinos from the Sun\n\nMeasurements of νe from the Sun, e.g., with SNO or Borexino [1], gave valuable insights into\nthe energy release processes in our home star. Moreover, they enabled the study of neutrino\nflavor changes in surroundings with varying matter density, allowing first tests of predictions\nfrom the MSW effect (e.g., see Figure 5 in [13]). Yet there are still open issues: The solar\nmetallicity, i.e., the composition of the Sun in terms of elements heavier than helium, is not\nconclusively determined. None of current solar models with different metallicities and opaci-\nties is in full agreement with neutrino measurements and observations from helioseismology.\nMoreover, looking at solar νe oscillations, the shape of the survival probability Pee(Eν) in the\ntransition from the matter-dominated region at higher energies to the vacuum-dominated region\nat lower energies is not yet fixed. Especially the transition region around 3MeV is sensitive to\neffects that can make Pee deviate from the MSW large mixing angle (LMA) prediction.\n\nSolar-ν measurements with the liquid scintillator detector of JUNO will be performed via\nelastic neutrino–electron-scattering as primary detection channel. They will benefit from the\nvery good energy resolution of the detector and the large target mass. Table 1 lists the expected\nsolar neutrino signal rates. However, for solar neutrino observations, the experiment needs\n\n4The GADZOOKS! upgrade [12] of the water Cherenkov detector Super-Kamiokande plans to dissolve the\nneutron absorber gadolinium in the 22.5 kt fiducial target mass. It is expected to increase the efficiency to tag\nthe final state IBD neutron to ∼ 80%, allowing to detect 3 to 5 DSNB νe events per year. For JUNO, this\nmakes GADZOOKS! a direct competitor in the quest for the first measurement of the DSNB.\n\n6 Magellan Workshop 2016\n\nSEBASTIAN LORENZ\n\n126 Magellan Workshop 2016\n\n\n\nNeutrino flux Signal rate (counts / day / kton)\npp ν 1378\n7Be-ν 517\npep ν 28\n8B-ν 4.5\n13N / 15O / 17F-ν 7.5 / 5.4 / 0.1\n\nTable 1: Expected solar neutrino signal rates at JUNO. Data cited from [4].\n\nto deal with radiopurity requirements that are more strict than for the MH measurement.\nTwo scenarios for low-energy solar neutrino detection, named baseline and ideal, are currently\nassumed. They are summarized in Table 14 of [4]: The baseline case corresponds approximately\nto the KamLAND solar phase purity level, yields a signal-to-background ratio of about 1:3\nand makes the solar 7Be neutrino branch accessible. In the ideal case, which assumes the\nBorexino phase-I purity level before 2010, the expected signal-to-background ratio is about\n2:1, probably giving a solar 7Be neutrino signal rate about three times larger than the total of\nall backgrounds.5\n\nBesides the achievement of high radiopurity, an efficient rejection of cosmogenic background\nfrom 11C and 10C decays is another important task. With the experiment’s overburden of\n∼ 700m, about 1860 and 35 counts per day and kton are expected, respectively.\n\nThe focus of JUNO concerning solar neutrinos would be measurements of the 7Be-ν and\n8B-ν fluxes. Together, the outcomes can help to break solar model ambiguities that cannot be\nresolved alone with results for neutrino fluxes from the CNO cycle in the Sun (see Figure 42 and\nFigure 43 in [4]). Moreover, a high-statistics measurement of the low-energy part of the 8B-ν\nspectrum around 3MeV will probe the Pee transition region, allowing the search for deviations\nfrom the MSW-LMA prediction.\n\nConclusions\n\nJUNO aims to determine the neutrino MH by precisely measuring the oscillatory fine structure\nin the survival probability of νe’s from nuclear reactors at a baseline length of 53 km. Once\ndata taking begins around 2020, the neutrino observatory will feature a 20 kt liquid scintillator\ndetector of unprecedented size and with a very good energy resolution. In case of a galactic\ncore-collapse SN at the benchmark distance of 10 kpc, about 5000 IBD events with energy,\ntime and partial flavor information will allow to test SN models. Concerning the DSNB, a 3σ\nevidence seems within reach after 10 years of measurement. If no positive DSNB signal is found,\nthe current upper DSNB flux limits will be significantly improved. A solar neutrino program of\nJUNO will decisively depend on the achieved radiopurity and cosmogenic background rejection\nefficiency. The focus will likely be on 7Be-ν and 8B-ν measurements, which can help to shed\nlight on the solar metallicity / opacity problem and on the transition region of the solar νe\nsurvival probability.\n\n5JUNO will profit from Borexino expertise in terms of radiopurity.\n\nMagellan Workshop 2016 7\n\nNEUTRINO ASTROPHYSICS WITH JUNO\n\nMagellan Workshop 2016 127\n\n\n\nReferences\n[1] G. Bellini et al. Final results of Borexino Phase-I on low energy solar neutrino spectroscopy. Phys. Rev.,\n\nD89(11):112007, 2014.\n\n[2] K. Bays et al. Supernova Relic Neutrino Search at Super-Kamiokande. Phys. Rev., D85:052007, 2012.\n\n[3] W. David Arnett, John N. Bahcall, Robert P. Kirshner, and Stanford E. Woosley. Supernova 1987A.\nAnn.Rev.Astron.Astrophys., 27(1):629–700, 1989.\n\n[4] Fengpeng An et al. Neutrino Physics with JUNO. J. Phys., G43(3):030401, 2016.\n\n[5] Zelimir Djurcic et al. JUNO Conceptual Design Report. arXiv: 1508.07166. 2015.\n\n[6] Alessandro Mirizzi, Irene Tamborra, Hans-Thomas Janka, Ninetta Saviano, Kate Scholberg, Robert Bollig,\nLorenz Hudepohl, and Sovan Chakraborty. Supernova Neutrinos: Production, Oscillations and Detection.\nRiv. Nuovo Cim., 39(1-2):1, 2016.\n\n[7] John F. Beacom, Will M. Farr, and Petr Vogel. Detection of supernova neutrinos by neutrino proton elastic\nscattering. Phys. Rev., D66:033001, 2002.\n\n[8] Graham M. Harper, Alexander Brown, and Edward F. Guinan. A New VLA-Hipparcos Distance to Betel-\ngeuse and its Implications. Astron. J., 135(4):1430, 2008.\n\n[9] Pietro Antonioli, Richard Tresch Fienberg, Fabrice Fleurot, Yoshiyuki Fukuda, Walter Fulgione, et al.\nSNEWS: The Supernova Early Warning System. New J.Phys., 6:114, 2004.\n\n[10] M. Apollonio et al. Determination of neutrino incoming direction in the CHOOZ experiment and supernova\nexplosion location by scintillator detectors. Phys. Rev., D61:012001, 2000.\n\n[11] Michael Wurm et al. The next-generation liquid-scintillator neutrino observatory LENA. Astropart. Phys.,\n35:685–732, 2012.\n\n[12] P. Fernandez. Status of GADZOOKS!: Neutron Tagging in Super-Kamiokande. Nucl. Part. Phys. Proc.,\n273-275:353–360, 2016.\n\n[13] G. Bellini et al. First evidence of pep solar neutrinos by direct detection in Borexino. Phys. Rev. Lett.,\n108:051302, 2012.\n\n8 Magellan Workshop 2016\n\nSEBASTIAN LORENZ\n\n128 Magellan Workshop 2016\n\n\n\nLatest results from LUNA experiment\n\nFrancesca Cavanna1, for the LUNA collaboration\n\n1INFN, Sezione di Genova, Via Dodecaneso 33, 16146 Genoa, Italy\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/10\n\nNuclear processes are responsible for energy generation that makes stars shine, and for the\nsynthesis of the elements in stars and also play a decisive role in explaining the chemical\ncomposition of the interstellar medium. Thermonuclear fusion reactions convert protons\ninto heavier elements from He to Fe. Deep underground in the Gran Sasso Laboratory\nthe key reactions of the proton-proton chain, the carbon-nitrogen-oxygen cycle and the\nneon-sodium cycle have been studied down to the energies of astrophysical interest. The\nlatest results are reviewed, together with future developments of underground nuclear\nastrophysics.\n\n1 Nuclear astrophysics at Gran Sasso\n\nAt the Laboratory for Underground Nuclear Astrophysics (LUNA), in the Gran Sasso National\nLaboratory, several cross sections have been measured in the past down to the energies of as-\ntrophysical interest [1, 2].\nAt low energy, the cross section of a charged particle induced reaction steeply drops with de-\ncreasing energy due to the Coulomb barrier. Generally, it has a very low value at the Gamow\nPeak, the energy region where most reactions occur; this prevents a direct measurement in a\nlaboratory on the Earths surface, where the signal to background ratio is too small because of\ncosmic ray interactions with detectors. The Gran Sasso underground facility is shielded against\ncosmic rays by a rock cover (1400 m thick) equivalent to 3800 m water, suppressing the muon\nand neutron flux by six and three orders of magnitude, respectively.\nThe measurement of the cross section for thermonuclear reactions require an experimental ap-\nparatus composed of an accelerator, a target, and a detection system.\nThe LUNA 400 kV accelerator delivers a proton beam of 500 µA or an alpha beam of 300 µA\nin the energy range of Ep−α = 50-400 keV. The ions can be sent into one of two different, beam\nlines, thereby allowing the parallel installation of solid and gas target setups.\nFor nuclear reactions in which charged particles are emitted, silicon detectors are usually\nadopted to make an in-beam measurement.\nFor nuclear reactions in which only γ-rays are emitted, the choice of the most suitable detec-\ntor depends on the physical information desired. The 4π bismuth germanate (BGO) summing\ncrystal used at LUNA can reach an efficiency as high as 70% for a 7-MeV γ ray, thus allowing\nthe measurement of extremely low reaction yields. However, the BGO’s energy resolution is\nvery poor and does not allow measurements of cascades and branching ratios to different levels\nbecause most of the γ-ray transitions are summed in a single peak. With a germanium detec-\ntor, the efficiency decreases to the level of a few per mil, but the energy resolution is much\n\nMagellan Workshop 2016 1Magellan Workshop 2016 129\n\n\n\nbetter, allowing complex γ-ray cascades to be disentangled. Moreover, angular distribution\nmeasurements can be made by placing the detector at different angles with respect to the ion\nbeam.\n\n2 Reactions studied at LUNA in the last years:\n\n2.1 17O(p,α)14N\n\nThis reaction plays an important role in the synthesis and abundance of key isotopes often\nused to test nucleosynthesis models of classical novae, Asymptotic Giant Branch (AGB) and\npost-AGB stars [3, 4, 5]. At energies of astrophysical interest its reaction rate is dominated [6]\nby a narrow and isolated resonance at Ep=70 keV. This resonance has been studied several\ntimes in the past, using both direct and indirect methods, as summarised in ref. [7]. However,\nthe picture painted in the literature is still not completely satisfying. The uncertainty in the\nresonance strength is not negligible (≈20%). Furthermore, published strength values obtained\nwith direct measurements have all been retracted or reanalysed [7].\nAn experimental campaign aimed at measuring the Ep=70 keV resonance in 17O(p, α)14N has\nbeen recently completed at the underground LUNA accelerator. The low background in the\nunderground environment has been exploited in order to carry out a direct investigation of\nthis weak (ωγ ≈ neV) resonance employing the thick-target yield technique. Protons have\nbeen accelerated on a solid Ta2O5 target and alpha particles detected at backward angles using\nan array of silicon detectors (Fig.1). The setup has been commissioned using the well-known\nEp=193 keV resonance in 17O(p, α)14N [8].\nA clear peak has been observed at Ep=71.5 keV. The alpha peak from the Ep=70 keV resonance\nappears where expected and has a signal significance higher than 5 sigma. Results of the\nanalysis indicate a resonance strength that is significantly higher than reported in previous\ninvestigations. Because of the importance of this resonance [6], LUNA results is expected to\nhave significant astrophysical consequences in a number of scenarios.\n\n2.2 22Ne(p,γ)23Na\n\nThe 22Ne(p,γ)23Na reaction is involved in the hydrogen burning neon-sodium (NeNa) cycle\nwhich is active in Red Giant Branch stars (Gamow peak 30-100keV), Asymptotic Giant Branch\nStars (AGB), classical novae (Gamow peak 100-600keV) [9] as well as in type Ia supernovae [10]\nand contributes to the nucleosynthesis of neon and sodium isotopes.\nThe 22Ne(p,γ)23Na is the NeNa cycle reaction with the most uncertain cross section. In the en-\nergy range relevant for astrophysics, the 22Ne(p,γ)23Na reaction rate was poorly known because\nof the contribution of a large number of resonances, many of which were never observed directly\n[11, 12], before the experiment performed at LUNA in 2015. The lowest-lying resonance with\na directly measured strength was the one at Elab\n\nR =436keV [13]; for the resonances below that\nenergy, only upper limits were reported in the literature [14, 15, 16]. The mere existence of\nseveral 23Na energy levels, e.g. the ones corresponding to the Ep = 215, 104, and 70 keV\nresonances, is even doubted [17].\nAn experimental campaign structured in two phases has been started in 2013 to measure di-\nrectly the resonance strength of several resonances below 400 keV. The data taking for the first\nphase, with High Purity Germanium Detectors (HPGe), has been concluded and the results\n\n2 Magellan Workshop 2016\n\nFRANCESCA CAVANNA\n\n130 Magellan Workshop 2016\n\n\n\nhave been published in 2015 [18]. The data taking for the second phase, with BGO detectors,\nhas been recently accomplished, the data analysis is still on going.\n\nPhase I: HPGe detectors\n\nThe setup consisted of two HPGe detectors, one at 90◦ with respect to the beam direction, and\nthe other one at 55◦ effective angle. The use of two high resolution detectors with well defined\nsolid angles allowed a measurement not only of the total resonance strength, but also of the\ndifferent branching ratios of the resonance decay. The two detectors were surrounded by a 4 cm\nthick copper shielding and a 25 cm thick lead shielding in order to suppress the environmental\nbackground [19].\nThe gamma detection efficiency has been determined in the energy range of interest for the\nmeasurement (i.e. 440 keV<Eγ < 9.5MeV) with radioactive sources at the lower energies (7Be,\n137Cs, 60Co and 88Y) and with the 14N(p,γ)15O nuclear reaction at the higher energies.\nEnriched neon-22 gas has been used for the measurements; for each resonance the yield profile\nas a function of the proton beam energy has been studied in 1-2 keV steps.\nThe resonances at Elab\n\nR =156.2, 189.5 and 259.7 keV have been detected for the first time. In\nFig. 2 the spectrum of the Elab\n\nR =156.2 keV resonance is shown together with the identified\n23Na transitions. Also the background lines are shown. Detailed branching schemes have been\ndeveloped for the new resonances, and in two cases, even a coincidence analysis of the two\nHPGe detectors has been possible. The thermonuclear reaction rate obtained with these new\nLUNA results [18] is shown in Fig. 3 compared with previous rates reported in literature.\n\nPhase II BGO detector\n\nIn phase II of the experiment, a high-luminosity 4π bismuth germanate summing crystal (BGO)\nwas used in order to address several low-energy resonances and the direct capture component, as\nwell. The typical γ-ray detection efficiency of 70% of this detector [20] enable either a positive\nconfirmation or a severe upper limit for the low-energy resonances that make up much of the\ndiscrepancy between the Sallaska and NACRE reaction rates (fig. 3).\nThe efficiency of the new setup was measured by means of four radioactive sources (7Be, 137Cs,\n60Co and 88Y) and the well known Ep=278 keV resonance of the 14N(p, γ)15O reaction.\nDuring the whole 2015 the data taking of this phase has been completed. The non resonant\ncontribution to the S-factor has been measured in an energy range from 200 keV up to 360 keV\nand the two purported resonances at 70 and 105 keV, for which only upper limits exist from\nthe HPGe phase, have been studied in detail.\n\n2.3 Future project: LUNA-MV\n\nIn the last years LUNA obtained important achievements in experimental Nuclear Astrophysics,\nmainly studying hydrogen burning reactions. In order to make a step forward and be able to\nmeasure reactions belonging to the helium burning that are important at higher temperatures\nin stars that ultimately means larger interaction energies, a higher voltage accelerator is neces-\nsary. With this aim, a new project just started, the LUNA-MV, which foresees the installation\nof a 3.5 MV machine in hall B of Gran Sasso Laboratories. The LUNA MV accelerator will\nprovide intense beams of H+, 4He+, 12C+ e 12C++ in the energy range: 200keV - 3.5MeV.\nThe accelerator will be built and tested by November 2017. Delivery at LNGS is scheduled for\n\nMagellan Workshop 2016 3\n\nLATEST RESULTS FROM LUNA EXPERIMENT\n\nMagellan Workshop 2016 131\n\n\n\nJanuary 2018. Finally, by July 2018 the accelerator will be installed and tested in hall B.\nThe first five years of experimental program foresee the measurement of 14N(p,γ)15O, 13C(α,n)16O,\n22Ne(α,n)25Mg and 12C+12C. Important achievements are expected soon.\n\n2.4 Tables and figures\n\nFigure 1: Picture of the target chamber used for the 17O(p,α)14N cross section measurement.\nAll silicon detectors are mounted. Protective foils are mounted on the inner dome in front of\neach detector.\n\n[keV]γE\n\n1000 2000 3000 4000 5000 6000\n\nC\no\n\nu\nn\n\nts\n\n1\n\n10\n\n210\n\nRun on resonance\n\nRun off resonance\n\nR->2391\n\n6\n5\n5\n3\n\nR->3914\n\n5\n0\n3\n0\n\n3914->0\n\n2391->0\n\n2391->440\n\n440->0\n\n214Bi\n\n214Bi\n\n40K\n\n214Bi\n\n214Bi\n\n214Bi\n\n22Na\n214Pb\n\ne+e-\n7Be\n\nFigure 2: (Color online) In red spectrum measured at Ep =162 keV, on top of the resonance at\n156.2 keV. The off-resonance spectrum (in blue) is superimposed.\n\n4 Magellan Workshop 2016\n\nFRANCESCA CAVANNA\n\n132 Magellan Workshop 2016\n\n\n\nN\n<\n\nv\n>\n\n/N\n<\n\nv\n>\n\n 1\n3\n \n\nT\n\nEp\nres [keV]\n\nSTARLIB 2013\nNACRE 1999\n\nLUNA\n\n10\n\n100\n\n101\n\n102\n\n103\n\n10\n\n 0.03  0.1  0.3  1\n\n71 105 156 215 323 479\n\nFigure 3: Thermonuclear reaction rate of LUNA [18], NACRE Collaboration [11] and STARLIB\ngroup [21], normalized to STARLIB group.\n\nReferences\n[1] H. Costantini, A. Formicola, G. Imbriani, M. Junker, C. Rolfs, and F. Strieder. LUNA: a laboratory for\n\nunderground nuclear astrophysics. Rep. Prog. Phys., 72(8):086301–+, August 2009.\n\n[2] Carlo Broggini, Daniel Bemmerer, Alessandra Guglielmetti, and Roberto Menegazzo. LUNA: Nuclear\nAstrophysics Deep Underground. Annu. Rev. Nucl. Part. Sci., 60:53–73, 2010.\n\n[3] M. Lugaro, A. I. Karakas, L. R. Nittler, C. M. O. Alexander, P. Hoppe, C. Iliadis, and J. C. Lattanzio. On\nthe asymptotic giant branch star origin of peculiar spinel grain OC2. Astron. Astrophys. , 461:657–664,\nJanuary 2007.\n\n[4] S. Palmerini, M. L. Sergi, M. La Cognata, L. Lamia, R. G. Pizzone, and C. Spitaleri. The RGB and AGB\nStar Nucleosynthesis in Light of the Recent 17O(p, α)14N and 18O(p, α)15N Reaction-rate Determinations.\nAstrophys. J. , 764:128, February 2013.\n\n[5] K. M. Nollett, M. Busso, and G. J. Wasserburg. Cool Bottom Processes on the Thermally Pulsing Asymp-\ntotic Giant Branch and the Isotopic Composition of Circumstellar Dust Grains. Astrophys. J. , 582:1036–\n1058, January 2003.\n\n[6] M. Q. Buckner, C. Iliadis, K. J. Kelly, L. N. Downen, A. E. Champagne, J. M. Cesaratto, C. Howard, and\nR. Longland. High-intensity-beam study of 17O(p ,γ )18F and thermonuclear reaction rates for 17O+p.\nPhys. Rev. C , 91(1):015812, January 2015.\n\n[7] M. L. Sergi, C. Spitaleri, M. La Cognata, L. Lamia, R. G. Pizzone, G. G. Rapisarda, X. D. Tang, B. Bucher,\nM. Couder, P. Davies, R. deBoer, X. Fang, L. Lamm, C. Ma, M. Notani, S. O’Brien, D. Roberson, W. Tan,\n\nMagellan Workshop 2016 5\n\nLATEST RESULTS FROM LUNA EXPERIMENT\n\nMagellan Workshop 2016 133\n\n\n\nM. Wiescher, B. Irgaziev, A. Mukhamedzhanov, J. Mrazek, and V. Kroha. Improvement of the high-\naccuracy 17O(p ,α )14N reaction-rate measurement via the Trojan Horse method for application to 17O\nnucleosynthesis. Phys. Rev. C , 91(6):065803, June 2015.\n\n[8] C. G. Bruno, D. A. Scott, A. Formicola, M. Aliotta, T. Davinson, M. Anders, A. Best, D. Bemmerer,\nC. Broggini, A. Caciolli, F. Cavanna, P. Corvisiero, R. Depalo, A. Di Leva, Z. Elekes, Z. Fülöp, G. Gervino,\nC. J. Griffin, A. Guglielmetti, C. Gustavino, G. Gyürky, G. Imbriani, M. Junker, R. Menegazzo, E. Napoli-\ntani, P. Prati, E. Somorjai, O. Straniero, F. Strieder, T. Szücs, and D. Trezzi. Resonance strengths in the\n17,18O(p, α)14,15N reactions and background suppression underground. Commissioning of a new setup for\ncharged-particle detection at LUNA. European Physical Journal A, 51:94, August 2015.\n\n[9] Christian Iliadis, Art Champagne, Jordi José, Sumner Starrfield, and Paul Tupper. The effects of ther-\nmonuclear reaction-rate variations on nova nucleosynthesis: A sensitivity study. Astrophys. J. Suppl. Ser.,\n142:105–137, 2002.\n\n[10] D. A. Chamulak, E. F. Brown, F. X. Timmes, and K. Dupczak. The Reduction of the Electron Abundance\nduring the Pre-explosion Simmering in White Dwarf Supernovae. Astrophys. J. , 677:160–168, April 2008.\n\n[11] Carmen Angulo, M. Arnould, M. Rayet, P. Descouvemont, D. Baye, C. Leclercq-Willain, A. Coc,\nS. Barhoumi, P. Aguer, C. Rolfs, R. Kunz, J. W. Hammer, A. Mayer, T. Paradellis, S. Kossionides,\nC. Chronidou, K. Spyrou, S. degl’Innocenti, G. Fiorentini, B. Ricci, S. Zavatarelli, C. Providencia,\nH. Wolters, J. Soares, C. Grama, J. Rahighi, A. Shotter, and M. Lamehi Rachti. A compilation of charged-\nparticle induced thermonuclear reaction rates. Nucl. Phys. A, 656:3–187, 1999.\n\n[12] C. Iliadis, R. Longland, A. E. Champagne, and A. Coc. Charged-particle thermonuclear reaction rates: III.\nNuclear physics input. Nucl. Phys. A, 841:251–322, 2010.\n\n[13] R. Depalo, F. Cavanna, F. Ferraro, A. Slemer, T. Al-Abdullah, S. Akhmadaliev, M. Anders, D. Bemmerer,\nZ. Elekes, G. Mattei, S. Reinicke, K. Schmidt, C. Scian, and L. Wagner. Strengths of the resonances at\n436, 479, 639, 661, and 1279 keV in the 22Ne(p ,γ ) 23Na reaction. Phys. Rev. C , 92(4):045807, October\n2015.\n\n[14] J. Görres, C. Rolfs, P. Schmalbrock, H. P. Trautvetter, and J. Keinonen. Search for low-energy resonances\nin 21Ne(p,γ)22Na and 22Ne(p,γ)23Na. Nucl. Phys. A, 385:57–75, 1982.\n\n[15] J. Görres, H. W. Becker, L. Buchmann, C. Rolfs, P. Schmalbrock, H. P. Trautvetter, A. Vlieks, J. W.\nHammer, and T. R. Donoghue. Proton-induced direct capture on 21Ne and 22Ne. Nucl. Phys. A, 408:372–\n396, October 1983.\n\n[16] S. E. Hale, A. E. Champagne, C. Iliadis, V. Y. Hansper, D. C. Powell, and J. C. Blackmon. Investigation\nof the 22Ne(p,γ)23Na reaction via (3He,d) spectroscopy. Phys. Rev. C, 65:015801, 2001.\n\n[17] R. B. Firestone. Nuclear Data Sheets for A = 23. Nucl. Data Sheets, 108:1–78, 2007.\n\n[18] F. Cavanna, R. Depalo, M. Aliotta, M. Anders, D. Bemmerer, A. Best, A. Boeltzig, C. Broggini, C. G.\nBruno, A. Caciolli, P. Corvisiero, T. Davinson, A. di Leva, Z. Elekes, F. Ferraro, A. Formicola, Z. Fülöp,\nG. Gervino, A. Guglielmetti, C. Gustavino, G. Gyürky, G. Imbriani, M. Junker, R. Menegazzo, V. Mossa,\nF. R. Pantaleo, P. Prati, D. A. Scott, E. Somorjai, O. Straniero, F. Strieder, T. Szücs, M. P. Takács,\nD. Trezzi, and LUNA Collaboration. Three New Low-Energy Resonances in the 22Ne (p ,γ )23Na Reaction.\nPhysical Review Letters, 115(25):252501, December 2015.\n\n[19] F. Cavanna et al. A new study of the 22Ne(p,γ)23Na reaction deep underground: Feasibility, setup, and\nfirst observation of the 186 keV resonance. Eur. Phys. J. A , 50:179, 2014.\n\n[20] C. Casella, H. Costantini, A. Lemut, B. Limata, D. Bemmerer, R. Bonetti, C. Broggini, L. Campajola,\nP. Cocconi, P. Corvisiero, J. Cruz, A. D’Onofrio, A. Formicola, Z. Fülöp, G. Gervino, L. Gialanella,\nA. Guglielmetti, C. Gustavino, G. Gyurky, A. Loiano, G. Imbriani, A. P. Jesus, M. Junker, P. Musico,\nA. Ordine, F. Parodi, M. Parolin, J. V. Pinto, P. Prati, J. P. Ribeiro, V. Roca, D. Rogalla, C. Rolfs,\nM. Romano, C. Rossi-Alvarez, A. Rottura, F. Schuemann, E. Somorjai, F. Strieder, F. Terrasi, H. P.\nTrautvetter, A. Vomiero, and S. Zavatarelli. A new setup for the underground study of capture reactions.\nNucl. Inst. Meth. A, 489:160–169, August 2002.\n\n[21] A. L. Sallaska, C. Iliadis, A. E. Champagne, S. Goriely, S. Starrfield, and F. X. Timmes. STARLIB: A\nNext-generation Reaction-rate Library for Nuclear Astrophysics. Astrophys. J. Suppl. Ser. , 207:18, July\n2013.\n\n6 Magellan Workshop 2016\n\nFRANCESCA CAVANNA\n\n134 Magellan Workshop 2016\n\n\n\nConnecting neutrino Astrophysics to Multi-TeV\n\nto PeV gamma-ray astronomy with TAIGA\n\nM. Tluczykont6, N. Budnev2, I. Astapov9, P. Bezyazeekov2, A. Bogdanov9, V. Boreyko10,\nM. Brückner8, A. Chiavassa4, O. Chvalaev2, O. Gress2, T. Gress2, O. Grishin2, A. Dyachok2,\nS. Epimakhov6, O. Fedorov2, A. Gafarov2, N. Gorbunov10, V. Grebenyuk10, A. Grinuk10,\nD. Horns6, A. Ivanova2, A. Kalinin10, N. Karpov1, N. Kalmykov1, Y. Kazarina2, N. Kirichkov2,\nS. Kiryuhin2, R. Kokoulin9, K. Komponiest9, A. Konstantinov1, E. Korosteleva1, V. Kozhin1,\nM. Kunnas6, L. Kuzmichev1,2, V. Lenok2, B. Lubsandorzhiev3, N. Lubsandorzhiev1,\nR. Mirgazov2, R. Mirzoyan5,2, R. Monkhoev2, R. Nachtigall6, E. Osipova1, A. Pakhorukov2,\nM. Panasyuk1, L. Pankov2, A. Petrukhin9, V. Platonov2, V. Poleschuk2, E. Popova1, A. Porelli8,\nE. Postnikov1, V. Prosin1, V. Ptuskin7, G. Rubtsov3, A. Pushnin2, V. Samoliga2, P. Satunin7,\nYu. Semeney2, A. Silaev1, A. Silaev (junior)1, A. Skurikhin1, V. Slucka10, C. Spiering8,\nL. Sveshnikova1, V. Tabolenko2, B. Tarashansky2, A. Tkachenko10, L. Tkachev10, D. Voronin2,\nR. Wischnewski8, A. Zagorodnikov2, V. Zurbanov2, I. Yashin9\n\n1 Skobeltsyn Institute of Nuclear Physics MSU, Moscow, Russia\n2 Institute of Applied Physics, ISU, Irkutsk, Russia\n3 Institute for Nuclear Research of RAN, Moscow, Russia\n4 Dipartimento di Fisica Generale Universiteta di Torino and INFN, Torino, Italy\n5 Max-Planck-Institute for Physics, Munich, Germany\n6 Institut für Experimentalphysik, Universität Hamburg, Germany\n7 IZMIRAN, Moscow Region, Russia\n8 DESY, Zeuthen, Germany\n9 NRNU MEPhI, Moscow,Russia\n10 JINR, Dubna, Russia\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/27\n\nRecent evidence for neutrinos in the PeV energy range from IceCube provides additional\nmotivation for the search for the most energetic Galactic accelerators. Gamma-ray astron-\nomy is a sound strategy to reach this goal, providing the energy range beyond 10TeV can\nbe covered at a sufficient sensitivity level. The energy spectra of most known gamma-ray\nemitters only reach up to few 10s of TeV. The HEGRA IACT installation reported evi-\ndence for gamma-ray energies from the Crab Nebula as high as 80TeV. Uncovering their\nspectral shape up to few 100s of TeV could answer the question whether some of these\nobjects are cosmic ray Pevatrons, i.e. Galactic PeV accelerators. Extending observations\nbeyond this energy range requires very large effective detector areas, as planned by the\nTAIGA collaboration.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 135\n\n\n\n1 Introduction\n\nGround based gamma-ray astronomy has rapidly evolved in the past decade, significantly af-\nfecting our knowledge of the non-thermal universe. Imaging atmospheric Cherenkov telescopes\n(IACT) led to a break-through with the first significant detection of gamma-rays in the TeV\nenergy range from the direction of the Crab Nebula [1]. Further innovations such as fast\nhigh-resolution PMT cameras (CAT) and the stereoscopic detection technique (HEGRA) led\nto currently existing instruments (H.E.S.S., MAGIC, and VERITAS). Today, more than 150\nsources of very high energy (VHE, E>100GeV) gamma-rays are known [2].\n\nAbove 100 GeV, the stereoscopic imaging technique is the method of choice. Existing and\nplanned instruments, such as the Cherenkov Telescope Array CTA [3], are relying on the IACT\ntechnique. Their optimal operation regime is typically located in the TeV energy range. The\ntotal energy coverage spans from few 10s of TeV to 100TeV. Beyond these energies, at multi-TeV\nto PeV energies, the gamma-ray universe is so far only poorly explored. Due to the powerlaw\nshape of source spectra, the observable flux drops quickly with increasing energy. Much larger\nsensitive areas than the ones from existing instruments are required for detection here.\n\nWhile imaging air Cherenkov telescopes have proven to be the instruments of choice in\nthe GeV to TeV energy range, large detector areas are more easily accessed with the (non-\nimaging) shower-front timing technique which also naturally provides large viewing angles.\nThe poor gamma-hadron separation power of shower front timing arrays can be compensated\nby a combination with small imaging air Cherenkov telescopes. Such a new hybrid detector\nconcept is currently being implemented by the TAIGA collaboration in the Tunka-valley in\nSiberia.\n\nIn the next section, the astrophysical motivation for multi-TeV to PeV astronomy is adressed.\nSubsequently, the strategy pursued by the TAIGA collaboration to access this energy range is\ndescribed.\n\n2 Astrophysics with ultra-high energy gamma-rays and\n\nneutrinos\n\nObservations in the multi-TeV energy range have a potential for discovery of new sources, and\nare particularly important to measure the spectral shape of known gamma-ray sources in the\ncutoff regime, where the objects reach their maximum acceleration energy. The accelerators of\ncosmic rays up to the knee region of the cosmic ray spectrum, the Pevatrons, are especially in the\nfocus of observations in the multi-TeV to PeV range. The paradigm of charged Galactic cosmic\nray production is the scenario of acceleration in shock fronts at the boundaries of expanding\nshells of supernova remnants (SNR). Nuclear scattering of the accelerated nuclei off the ambient\nmedium produce neutral and charged pions which subsequently decay into gamma-rays and\nneutrinos at energies of about a factor 10 less than the primary cosmic ray particles (due to the\ntypical inelasticity of the hadronic interaction). A Pevatron accelerating charged cosmic ray\nparticles up to cutoff energies of 3PeV would produce hard gamma-ray and neutrino spectra\nup to cutoff energies of roughly 300TeV.\n\nWhile neutrinos from an astrophysical source are an unambiguous smoking-gun signature for\nhadronic processes, this is not necessarily the case for gamma-rays. From few 10s of GeV up to\nabout 10 TeV, the origin of a gamma-ray signal is difficult to identify. Gamma-rays can also be\nproduced in leptonic scenarios, in which electrons scatter off low energy seed photons, boosting\n\n2 Magellan Workshop 2016\n\nMARTIN TLUCZYKONT et al.\n\n136 Magellan Workshop 2016\n\n\n\nthe photons up to TeV energies (Inverse Compton scattering). Above few 10s of TeV, the\ninverse Compton effect becomes less efficient because of Klein-Nishina suppression, resulting in\nsoft spectra. As opposed to that, nucleonic gamma-ray production increases efficiency because\nof increasing inelastic cross-sections, resulting in harder spectra. Therefore, the observation of\nhard gamma-ray spectra in this energy regime would resolve the leptonic/hadronic ambiguity\nand represent a smoking-gun signature of cosmic ray acceleration. The difference in leptonic\nand hadronic spectra is illustrated qualitatively in Figure 1. Observations of the Galactic center\nby H.E.S.S. [4], and recent evidence for astrophysical neutrinos from IceCube [5, 6] are further\nmotivation for a search for multi-TeV to PeV gamma-rays.\n\nAs reported in [6], the IceCube neutrinos were detected in the energy range from 100TeV to\nabout 1 PeV. The data are reported to be in favour of an astrophysical interpretation. Among\nthe 37 neutrino events detected by IceCube, 5 were speculated to be associated with extreme\nBlazars by other authors [7]. Based on this tentative association, [8] have presented model cal-\nculations for gamma-ray fluxes expected in a hadronic emission scenario and using the neutrino\nevents as flux constraints. The predicted fluxes (see Figure 1) are reaching their maximum\nbetween 1 and 10PeV, a region so far not accessed by gamma-ray experiments.\n\nenergy/TeV\n\n1−10 1 10 210 310 410\n\n-1\n s\n\n-2\nin\n\nte\ngr\n\nal\n fl\n\nux\n / \n\ner\ng \n\ncm\n\n14−10\n\n13−10\n\n12−10\n\n11−10\n\n10−10\n\n0π\n\nInv. Compton\n\nPetropoulo et al. 2015\n\nFigure 1: Extrapolations to the multi-\nTeV regime: π0-decay spectrum (solid\ncurve, powerlaw with exponential cut-\noff, no attenuation), inverse Compton\nspectrum (dashed curve, double expo-\nnential cutoff). Curves are scaled to\ndata from the H.E.S.S. Galactic plane\nsurvey [9] (grey circles) and the Mi-\nlagro source MGROJ1908+06 (black\nsquare: Milagro [10], black circles:\nH.E.S.S. [9]). Open circles: hypothet-\nical gamma-ray fluxes from Blazars\npossibly associated with ν events [8].\n\nWhile gamma-rays and neutrinos must both be produced in the same astrophysical envi-\nronments, their detection requires slightly different approaches on considerably different scales.\nGamma-rays from GeV to PeV energies can be detected on the ground via extended air shower\n(EAS) detectors, which measure either the particles (scintillators, water Cherenkov detectors)\nproduced in electromagnetic interactions in the EAS, or their secondary Cherenkov light emis-\nsion (IACTs, timing arrays). Neutrinos can be detected in large volume detectors via their neu-\ntral current or charged current interaction with the ambient medium. While for both particle\ntypes the detection methods mostly rely on the measurement of Cherenkov light emission from\nsecondary particles resulting from the interaction in the atmosphere or the detector medium,\nthe scale of the required instrumentation is much larger for neutrino detection due to the low\ncross section of the weak interaction.\n\nThe much higher cross section of the electromagnetic interaction makes the detection of\ngamma-rays possible on smaller scales. On the other hand and for the same reason, gamma-ray\nfluxes can be strongly attenuated in astrophysical environments and during their propagation\nto Earth. Gamma-rays can interact with photons from low energy photon fields and produce\n\nMagellan Workshop 2016 3\n\nCONNECTING NEUTRINO ASTROPHYSICS TO MULTI-TEV TO PEV GAMMA-RAY . . .\n\nMagellan Workshop 2016 137\n\n\n\nelectron-positron pairs, therewith reducing the observable flux at Earth as compared to the\nsource intrinsic flux. The attenuation effect depends on the gamma-ray energy, the energy of the\nlow energy photon, and the distance travelled. The absorption of TeV gamma-rays from Active\nGalactic Nuclei (AGN) has been extensively studied by Fermi-LAT and the current generation of\nIACT experiments, providing limits on the density of the extragalactic background light (EBL)\nin the wavelength range from 0.1µm to 5µm. Spectroscopy of nearby AGN in the energy range\nfrom 5TeV to 50TeV would access the wavelength band from 5µm to 50µm. It was shown\nby [11] that gamma-rays in this energy range from nearby AGN might undergo less absorption\nby pair production than expected. Possible explanations for this effect could be mixing of\nphotons with light pseudoscalars or effects of breaking of Lorentz-invariance [12], which would\nlead to a reduction in the effective cross-section for electron positron pair production due to a\nmodification of the kinematics of the interaction (see e.g. [13]).\n\nIn the ultra-high energy regime, the absorption effect is strong enough to be relevant also for\nGalactic sources. [14] have shown that the dominant photon fields are the interstellar radiation\nfield (ISRF), and the cosmic microwave background (CMB). The strength of absorption by\npair production on the ISRF reaches a maximum at about 100TeV, and can vary from few\npercent for nearby sources up to 50% for faraway sources (i.e. located inside the Galactic disk\nat a distance of 20 kpc beyond the Galactic center). The absorption caused by the CMB is\nmaximal at about 3PeV, where more than 90% of the gamma-ray flux from a faraway Galactic\nsource is absorbed. Beyond 3PeV, the Galaxy becomes transparent to gamma-rays again. A\nmeasurement of gamma-ray spectra modified by the absorption effect can provide an estimate\nof the ISRF density. Since the CMB density is very well known, such a measurement up to PeV\nenergies could provide a new method to measure the distance of Galactic sources from their\nspectral shape in the gamma-ray energy range.\n\n3 Accessing the ultra-high energy gamma-ray regime\n\n3.1 Detection techniques\n\nThe questions described above can be adressed by accessing the ultra-high energy gamma-ray\nregime, i.e. energies beyond 10TeV and up to the PeV regime. While it is in principle possible\nto reach energies beyond 100TeV with IACTs, the existing and planned instruments are limited\nby their instrumented area, the large number of required channels per km2, and their dynamical\nrange. Even CTA will only be able to do spectroscopy beyond 100TeV by investing a significant\nfraction of its available observation time. Considering a flux of only few photons per km2 per\nhour even from the strongest gamma-ray sources, a very large intrumented area is obviously\nthe key to making spectroscopic studies at these energies.\n\nGround-based non-imaging EAS detectors measure the arrival-time distribution and density\nof particles or photons on the ground. A high reconstruction quality (and thus sensitivity)\ncan be achieved using the density and arrival-time distribution of particles or photons on the\nground. While Milagro, HAWC [15], and LHAASO [16] mainly use charged particle detectors\nto access the multi-TeV regime, the timing of the air shower front is best measured using the\nCherenkov photons emitted by the secondary air shower particles. Air Cherenkov timing-arrays\ncan provide an angular resolution of the order of 0.1◦ and relative energy resolution of better\nthan 15% (see, e.g. [17]), as compared to 1◦ and 100% for particle shower front sampling\ntechniques. However, the rejection of the predominant hadronic background is difficult using\n\n4 Magellan Workshop 2016\n\nMARTIN TLUCZYKONT et al.\n\n138 Magellan Workshop 2016\n\n\n\nthe timing-array technique alone. This is better achieved by IACTs, using the image shape for\nhadron rejection.\n\n3.2 HiSCORE\n\nOne concept to access the multi-TeV energy range with a very large area wide angle instrument\nis the HiSCORE experiment [18]. HiSCORE consists of an array of timing stations, measuring\nthe air Cherenkov photon front emitted by the secondary particles in the EAS. A HiSCORE\ndetector station consists of four 8 inch photomultipliers (PMTs), each equipped with a 40 cm\nWinston cone light concentrator, built from flexible segments of reflective foil sheets. The\nlight collection area per station is 0.5m2 and the solid angle covered is 0.6 sr. A picture of a\nHiSCORE station is shown on the left side of Figure 2. The analog signals from the anodes and\nthe next to last dynode are digitized in the GHz regime using a custom developed readout board\nbased on the DRS 4 chip. Before readout, an analog summator board is dividing the analog\nsignals, and summing the anodes. The analog sum is used for triggering. The stations can be\nmanually tilted along the N-S axis in order to access different parts of the sky. A relative time\nsynchronization at sub-ns level is required over the full scale of the array. Two independent\ntime-calibration methods were successfully employed, reaching the required accuracy [19].\n\nA first implementation of the HiSCORE concept was realized by the Tunka-HiSCORE group.\nToday, the HiSCORE detector is operated as part of the TAIGA collaboration and consists of\n28 HiSCORE timing stations distributed over an area of 0.25 km2 in the Tunka valley in Siberia.\nThe layout of the 28 station array is shown in Figure 2. An extension to a 0.6 km2 array and a\ncombination with an IACT is planned between 2016 and 2017. This combination is described\nin the next section. First results from the HiSCORE timing array were presented recently and\n\nX / m\n-500 -400 -300 -200 -100 0 100 200 300 400 500\n\nY\n / \n\nm\n\n-500\n\n-400\n\n-300\n\n-200\n\n-100\n\n0\n\n100\n\n200\n\n300\n\n400\n\n500\n\nFigure 2: Left: Picture of a HiSCORE timing station. Right: Layout of the 28 HiSCORE\nstations (Filled squares). The optical stations of Tunka-133 are shown as filled circles.\n\nwill be the subject of proceedings publications [20].\n\nMagellan Workshop 2016 5\n\nCONNECTING NEUTRINO ASTROPHYSICS TO MULTI-TEV TO PEV GAMMA-RAY . . .\n\nMagellan Workshop 2016 139\n\n\n\n3.3 TAIGA: combining imaging and timing\n\nTAIGA [21, 22] stands for Tunka Advanced Instrument for cosmic ray and Gamma ray Astron-\nomy. The TAIGA experiment is planning to combine the HiSCORE timing array with 10 small\nsized imaging air Cherenkov telescopes. A first IACT is currently under construction on the\nTunka site.\n\nA TAIGA IACT consists of a Davies-Cotton mirror dish with 34 mirror facets of a diameter\nof 60 cm each. The mirror dish diameter will be 4.3m. A PMT camera is mounted at a focal\ndistance of 4.75m. The camera consists of 547 PMTs with a 0.36 ◦ field of view (foV) per pixel,\nresulting in a total field of view of 9.72 ◦ in diameter. The PMTs will be arranged with a 30mm\ncenter-to-center distance in 11 hexagonal rings around the central pixel. The expected energy\nthreshold for monoscopic events is 500GeV. Near the threshold and up to few 10s of TeV,\nthe IACTs will operate in stand alone mode, using the classical imaging technique, performing\nmonitoring observations of transient sources. Above few 10s of TeV and in the overlapping\npart of the IACT foV with the larger foV of the HiSCORE timing array, the IACTs will\noperate together with the timing array in a new promising hybrid operation mode. The timing\nstations will trigger independently from the individual IACTs, and data will be merged using\nsynchronized (sub-ns) event time-stamps.\n\nThe basic idea for a combination of the imaging and timing techniques is to use the timing\narray as estimator of the air shower core position and direction of the primary particle and to\nprovide these parameters to the IACT for improvement of the monoscopic image analysis. Since\na large instrumented area is the key aspect for reaching the multi-TeV range, the telescopes will\nbe placed at a distance of roughly 600m from each other. In this configuration all telescopes\nwill operate in monoscopic mode for zenith angles up to 60◦. Since the direction of the air\nshower is not unambiguously known to a monoscopic IACT, the timing array will provide\nthis information, therewith allowing to reject a large fraction of isotropic hadronic background\nevents. Furthermore, the knowledge of the core impact position from the timing array will allow\nto introduce the hybrid scaled width parameter, similarly to the classical stereoscopic mean\nscaled width parameter used in standard imaging analyses based on the Hillas parameters.\nThis concept was previously described in [23, 24] and was also presented at this conference [25].\nUsing the hybrid scaled width and further gamma-hadron separation parameters based on the\nIACT image analysis as well as the timing array, we expect to reach a gamma-hadron separation\nquality factor of the order of 3-5. The hybrid imaging-timing point-source sensitivity of a\n0.6 km2 TAIGA detector is estimated to reach a sensitivity of 5 · 10−13 erg cm−2 s−1 at 100TeV\nafter 200 h of observation time. The integral sensitivities to point sources for this, and a 5 km2\n\ndetector stage (after 1000h) are shown in Figure 3 and compared to the sensitivities from other\nexperiments. Already the upcoming 0.6 km2 array will significantly extend the spectra of known\nsources into the multi-TeV regime.\n\n4 Summary\n\nWhile the field of gamma-ray astronomy has made tremendous progress over the past decades,\nthe observation window in the multi-TeV to PeV regime needs yet to be covered by sensitive\ninstruments. This energy range is the key to different astrophysical questions, aforemost the\nquestion of the origin of cosmic rays and the localization of the highest energy Galactic accelera-\ntors. Recent observations from H.E.S.S. and the IceCube neutrino events are further motivating\nthis search.\n\n6 Magellan Workshop 2016\n\nMARTIN TLUCZYKONT et al.\n\n140 Magellan Workshop 2016\n\n\n\nenergy/TeV\n\n1−10 1 10 210 310 410\n\n-1\n s\n\n-2\nin\n\nte\ngr\n\nal\n fl\n\nux\n / \n\ner\ng \n\ncm\n\n14−10\n\n13−10\n\n12−10\n\n11−10\n\n10−10\n\nPetropoulo et al. 2015\n\nFigure 3: Left: Integral flux survey sensitivities of TAIGA, combining timing array stations\nwith IACTs, for a 0.6 km2 array after 200 h of observation time (solid red line, requiring 5σ\nand at least 10 gamma-rays) and a 5 km2 array after 1000h of observation time (dashed red\nline). For comparison, further sensitivities are shown: CTA survey (grey area [26]), HAWC\n([15] dash-dotted line), CTA pointed observations (large exposure on specific sources, dashed\ngrey). Data points as in Fig. 1. Right: Photograph of the first TAIGA IACT, shortly before\ntransportation to the Tunka valley.\n\nThe TAIGA collaboration is aiming at this goal, using a novel hybrid approach, combining\nthe air Cherenkov timing technique with the imaging technique. The upcoming 0.6 km2 stage\nof TAIGA will for the first time allow to probe the ultra high energy gamma-ray regime at a\nsensitivity level comparable to the sensitivity of existing instruments at lower energies.\n\n5 Acknowledgments\n\nWe acknowledge the support of the Russian Federation Ministry of Education and Science\n(agreements N 14.B25.31.0010, zadanie No 3.889.2014/K), the Russian Foundation for Ba-\nsic research (13-02-00214, 13-02-12095, 15-02-10005, 15-02-05769), the Helmholtz association\n(grant HRJRG-303), and the Deutsche Forschungsgemeinschaft (grant TL 51-3).\n\nReferences\n\n[1] T. C. Weekes, M. F. Cawley, D. J. Fegan, et al. Observation of TeV gamma rays from the Crab nebula\nusing the atmospheric Cerenkov imaging technique. ApJ, 342:379–395, 1989.\n\n[2] S. Wakely and D. Horan. The TeV catalogue, TeVCat, http://tevcat.uchicago.edu/.\n\n[3] B. S. Acharya, M. Actis, T. Aghajani, G. Agnetta, J. Aguilar, et al. Introducing the CTA concept.\nAstr.Part.Phys., 43:3–18, 2013.\n\nMagellan Workshop 2016 7\n\nCONNECTING NEUTRINO ASTROPHYSICS TO MULTI-TEV TO PEV GAMMA-RAY . . .\n\nMagellan Workshop 2016 141\n\n\n\n[4] HESS Collaboration, A. Abramowski, F. Aharonian, F. A. Benkhali, A. G. Akhperjanian, E. O. Angüner,\nM. Backes, A. Balzer, Y. Becherini, J. B. Tjus, and et al. Acceleration of petaelectronvolt protons in the\nGalactic Centre. Nature, 531:476–479, 2016.\n\n[5] M. G. Aartsen, R. Abbasi, Y. Abdou, M. Ackermann, et al. First Observation of PeV-Energy Neutrinos\nwith IceCube. Physical Review Letters, 111(2):021103, July 2013.\n\n[6] M. G. Aartsen et al. Observation of High-Energy Astrophysical Neutrinos in Three Years of IceCube Data.\nPhys. Rev. Lett., 113:101101, 2014.\n\n[7] P. Padovani, E. Resconi, P. Giommi, B. Arsioli, and Y. L. Chang. Extreme blazars as counterparts of\nIceCube astrophysical neutrinos. MNRAS, 457:3582–3592, 2016.\n\n[8] M. Petropoulou, S. Dimitrakoudis, P. Padovani, A. Mastichiadis, and E. Resconi. Photohadronic origin of\nγ -ray BL Lac emission: implications for IceCube neutrinos. MNRAS, 448:2412–2429, 2015.\n\n[9] F. Aharonian, A. G. Akhperjanian, G. Anton, U. Barres de Almeida, Bazer-Bachi, et al. Detection of very\nhigh energy radiation from HESS J1908+063 confirms the Milagro unidentified source MGRO J1908+06.\nA&A, 499:723–728, 2009.\n\n[10] A. A. Abdo, B. Allen, D. Berley, S. Casanova, C. Chen, et al. TeV Gamma-Ray Sources from a Survey of\nthe Galactic Plane with Milagro. ApJ, 664:L91–L94, 2007.\n\n[11] M. Meyer, D. Horns, and M. Raue. Indications for a low opacity universe from Fermi-LAT data. In F. A.\nAharonian, W. Hofmann, and F. M. Rieger, editors, American Institute of Physics Conference Series,\nvolume 1505 of American Institute of Physics Conference Series, pages 598–601, 2012.\n\n[12] D. Colladay and V. A. Kostelecký. Lorentz-violating extension of the standard model. Phys. Rev. D,\n58(11):116002, 1998.\n\n[13] G. Rubtsov, P. Satunin, and S. Sibiryakov. Prospective constraints on Lorentz violation from ultrahigh-\nenergy photon detection. Phys. Rev. D, 89(12):123011, 2014.\n\n[14] I. V. Moskalenko, T. A. Porter, and A. W. Strong. Attenuation of Very High Energy Gamma Rays by the\nMilky Way Interstellar Radiation Field. ApJL, 640:L155–L158, 2006.\n\n[15] G. Sinnis. HAWC: A Next Generation VHE All-Sky Telescope. In F. A. Aharonian, H. J. Völk, and\nD. Horns, editors, Heidelberg Gamma-Ray Symposium, volume 745 of AIP, page 234, 2005.\n\n[16] Shuwang Cui, Ye Liu, Yujuan Liu, and Xinhua Ma. Simulation on gamma ray astronomy research with\nLHAASO-KM2A. Astr.Part.Phys., 54:86 – 92, 2014.\n\n[17] D. Hampf, M. Tluczykont, and D. Horns. Event reconstruction techniques for the wide-angle air cherenkov\ndetector HiSCORE. NIMA, 712:137, 2013.\n\n[18] M. Tluczykont, D. Hampf, D. Horns, D. Spitschan, L. Kuzmichev, V. Prosin, C. Spiering, and R. Wis-\nchnewski. The HiSCORE concept for gamma-ray and cosmic-ray astrophysics beyond 10 TeV. Astroparticle\nPhysics, 56:42–53, 2014.\n\n[19] O. Gress, I. Astapov, N. Budnev, et al. The wide-aperture gamma-ray telescope taiga-hiscore in the tunka\nvalley: design, composition and commissioning., 2016. To appear in proc. of VCI2016 - The 14th Vienna\nConference on Instrumentation.\n\n[20] 2016. M. Tluczykont et al. and L. Kuzmichev et al. for the TAIGA collaboration, 16th RICAP, Frascati,\nItaly, https://agenda.infn.it/.\n\n[21] R. Mirzoyan. TAIGA detector for cosmic and gamma rays for TeV - EeV energy range. In 40th COSPAR\nScientific Assembly, volume 40 of COSPAR Meeting, 2014.\n\n[22] N. Budnev et al. The TAIGA experiment: from cosmic ray to gamma-ray astronomy in the Tunka valley,\n2016.\n\n[23] M Tluczykont, I Astapov, N Barbashina, et al. Towards gamma-ray astronomy with timing arrays. Journal\nof Physics: Conference Series, 632(1):012042, 2015.\n\n[24] M Kunnas, I Astapov, N Barbashina, et al. Simulation of the hybrid tunka area instrument for cosmic rays\nand gamma-ray astronomy (taiga). Journal of Physics: Conference Series, 632(1):012040, 2015.\n\n[25] M. Kunnas et al., 2016. These proceedings.\n\n[26] G. Dubus et al. Surveys with the Cherenkov Telescope Array. Astr.Part.Phys., 43:317, 2013.\n\n8 Magellan Workshop 2016\n\nMARTIN TLUCZYKONT et al.\n\n142 Magellan Workshop 2016\n\n\n\nSimulation of imaging air shower Cherenkov tele-\n\nscopes as part of the TAIGA Project\n\nM.Kunnas6, I. Astapov9, P. Bezyazeekov2, A. Bogdanov9, V. Boreyko10, M. Brückner11,\nN. Budnev2, A. Chiavassa4, O. Chvalaev2, O. Gress2, T. Gress2, O. Grishin2, A. Dyachok2,\nS. Epimakhov6, O. Fedorov2, A. Gafarov2, N. Gorbunov10, V. Grebenyuk10, A. Grinuk10,\nD. Horns 6, A. Ivanova2, A. Kalinin10, N. Karpov1, N. Kalmykov1, Y. Kazarina2, N. Kirichkov2,\nS. Kiryuhin2, R. Kokoulin9, K. Komponiest9, A. Konstantinov1, E. Korosteleva1, V. Kozhin1,\nL. Kuzmichev1,2, V. Lenok2, B. Lubsandorzhiev3, N. Lubsandorzhiev1, R. Mirgazov2,\nR. Mirzoyan5,2, R. Monkhoev2, R. Nachtigall6, E. Osipova1, A. Pakhorukov2, M. Panasyuk1,\nL. Pankov2, A. Petrukhin9, V. Platonov2, V. Poleschuk2, E. Popova1, A. Porelli8, E. Postnikov1,\nV. Prosin1, V. Ptuskin7, G. Rubtsov3, A. Pushnin2, V. Samoliga2, P. Satunin7, Yu. Semeney2,\nA. Silaev1, A. Silaev (junior)1, A. Skurikhin1, V. Slucka10, C. Spiering8, L. Sveshnikova1,\nV. Tabolenko2, B. Tarashansky2, A. Tkachenko10, L. Tkachev10, M. Tluczykont6, D. Voronin2,\nR. Wischnewski8, A. Zagorodnikov2, V. Zurbanov2, I. Yashin9\n\n1 Skobeltsyn Institute of Nuclear Physics MSU, Moscow, Russia\n2 Institute of Applied Physics, ISU, Irkutsk, Russia\n3 Institute for Nuclear Research of RAN, Moscow, Russia\n4 Dipartimento di Fisica Generale Universiteta di Torino and INFN, Torino, Italy\n5 Max-Planck-Institute for Physics, Munich, Germany\n6 Institut fr Experimentalphysik, University of Hamburg, Germany\n6 Institut für Experimentalphysik, Universität Hamburg, Luruper Chaussee 149, 22761 Ham-\nburg, Germany\n7 IZMIRAN, Moscow Region, Russia\n8 DESY, Zeuthen, Germany\n9 NRNU MEPhI, Moscow,Russia\n10 JINR, Dubna, Russia\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/33\n\nThe Tunka Advanced International Gamma-ray and Cosmic ray Astrophysics (TAIGA)\nproject aims at observation of cosmic rays beyond 100 TeV and gamma rays above 1 TeV\nvia observation of the extensive air showers (EAS) caused in the atmosphere. Two com-\nmon detection techniques are timing arrays and imaging air shower cherenkov telescopes\n(IACT). Timing arrays yield good directional and energy reconstruction, but provide only\nmediocre gamma-hadron-separation at their energy threshold. IACTs are good at separa-\ntion, but since a stereoscopic view of a shower is needed for high reconstruction accuracy,\nit is difficult to achieve the large effective areas needed for ultra high energy observations.\n\nIn this work we present the simulations performed to explore and optimize our IACT design\nand the first steps towards hybrid reconstruction.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 143\n\n\n\n1 Motivation\n\nThe highest energies in the gamma ray spectrum are a very interesting domain of cosmic\naccelerators. Many kinds of sources have already been studied up to a range of tens of TeV,\nbut latest results from experiments like IceCube hint at the existence of sources accelerating\nparticles up to the PeV range, so-called Pevatrons. While the charged particles from such\nsources are lost in the interstellar magnetic fields, they produce pions in the ambient medium\nwhich subsequently decay into gamma-rays. These gammas carry less energy than the original\npartice had (O(10) less), so a gamma ray source at >100TeV would be a Pevatron. For further\ndetails on Pevatrons, see [1] (these proceedings).\n\nThe flux of gammas with such a high energy is low, making it necessary to instrument large\nareas on the Earth’s surface to reach sufficiently large effective areas needed for sensitivity. This\npaper presents a method to address this challenge.\n\n2 A combination of known approaches\n\nTwo common and well-understood methods of observing the high energy gamma ray sky are\ntiming arrays and imaging air cherenkov teleskopes (IACTs).\n\nTiming arrays sample the Cherenkov light front of EAS. Core position and direction of\nincidence are calculated from the arrival time distribution of the air shower front. The com-\nparatively low number of readout channels per km2 is an advantage for instrumenting the very\nlarge areas needed for proper sensitivity.\n\nIACT systems use two or more telescopes with (most often tesselated) mirrors and multi-\nchannel cameras to take images of the EAS. Using a Hillas-type image shape analysis[2], a good\ndistinction between EAS initiated by gammas and EAS caused by the proton background is\npossible. The downside of this method is that a stereoscopic view of a shower is needed to\nreconstruct impact core position and shower angle with sufficient accuracy. This sets an upper\nlimit to the spacing of the telescopes (about 300m) since at least two telescopes need to be inside\nthe Cherenkov light pool to get more than one shower image. Consequently, covering large aeras\nwith IACTs requires a large number of detection and readout channels per instrumented km2.\n\nThe idea behind the TAIGA project is to combine both techniques to use both their strengths\nwhile compensating for their weaknesses. A timing array will determine arrival direction and\nimpact point of the air shower, which is then used to interpret the IACT camera image to\ndetermine the nature of the EAS primary. Since the directional information is reconstructed by\nthe timing array, the need for stereoscopy does not apply and the spacing between the telescopes\ncan be doubled.\n\nThe TAIGA experiment will consist of the already deployed HiSCORE timing array and\ncustom designed IACTs.\n\nHiSCORE stations contain 4 photomultiplier tubes (PMTs, 8”diameter) each, looking di-\nrectly into the night sky through winston cones. 28 stations have been deployed at Tunka\nValley, Russia (51◦ 48’ 35” N, 103◦ 04’ 02” E, 675 m a.s.l.) with a spacing between 100m -\n150m covering about 0.25 km2 and have been in operation since winter 2014. Further extension\nis envisaged up to 3 km2.\n\nThe custom designed IACTs will be Davies-Cotton telescopes with a 4.3m tesselated mirror\ndish, a focal length of 4.74m and a 540 PMT pixel camera with a field of view of 10◦ diameter.\nThese telescopes will be placed at a 600m spacing, a prototype is already in construction.\n\n2 Magellan Workshop 2016\n\nMAIKE KUNNAS et al.\n\n144 Magellan Workshop 2016\n\n\n\n3 Simulations\n\nTo evaluate the detector design, a Monte Carlo (MC) simulation was done in three steps.\nFirst, the shower itself was simulated using CORSIKA-6990 [3](IACT option for generation of\nCherenkov light, hadronic interaction models QGSJET [4] and Gheisha [5]). The shower data\nis then evaluated independently by the simulation routines for HiSCORE and for the IACTs.\n\nDetection by HiSCORE is calculated with the sim score[6] code, a custom simulation soft-\nware based on the IACT package performing a full detector simulation including all response\nfunctions. From the simulation of gamma-ray induced EAS, the resolution for core position,\nincidence angle, and reconstructed energy is determined.\n\nThe IACT simulation is done with sim telarray[7] and performs a full raytracing of the\nCherenkov photons through the telescopes’ optics. This takes the telescope design properties\ninto account, e.g. mirror dish shape, mirror reflectivity and (mis-)alignment, camera size,\nshadowing by masts and camera, and camera PMT efficiency. Last simulation step is the\naddition of noise from NSB and the electronic parts.\n\nAfter generation of camera images, sim telarray carries out the image cleaning and the\ncalculation of the Hillas parameters and, if more than one telescope triggered, stereoscopic re-\nconstruction of direction, incidence angle, and energy, even though the stereoscopic evaluations\nare not necessary for our studies.\n\nThe simulations presented in these proceedings are the IACT’s point spread function, which\nis a property of the telescope design, and first insights into the quality of the gamma hadron\nseparation. Please note all results presented here are preliminary.\n\n3.1 Point Spread Function (PSF)\n\nFigure 1: IACT PSF for angles of 0◦-5◦ from the optical axis in 1D projection and in greyscale.\nVertical lines indicate each spot’s projected 86% range. Perfectly aligned mirrors are assumed.\nThe finger-like structures are an effect of the outermost mirror segments.\n\nSimulation To evaluate the intended design of the IACTs, the sim telarray ray tracing rou-\ntine was used to simulate light from an infinitly far away point source. The image of the source\nin the camera plane is not perfect due the abberations from the mirrors, and the resulting in-\ntensity distribution is called the PSF. To find the theoretical lower limit of the PSF, we assume\nperfect mirrors, no misalignments and no NSB.\n\nMagellan Workshop 2016 3\n\nSIMULATION OF IMAGING AIR SHOWER CHERENKOV TELESCOPES AS PART OF THE . . .\n\nMagellan Workshop 2016 145\n\n\n\n 0\n\n 0.02\n\n 0.04\n\n 0.06\n\n 0.08\n\n 0.1\n\n 0.12\n\n 0.14\n\n 0.16\n\n 0.18\n\n 0  1  2  3  4  5\n\nP\nS\n\nF\n R\n\nM\nS\n\n [d\neg\n\n]\n\nAngle from optical axis [deg]\n\nTAIGA PSF vs. prediction\n\ntess. 0.03 [1]\n\ntess. 0.13 [this work]\n\nFigure 2: Comparison between analyt-\nical and simulated predictions for the\nPSF for F/D = 1.1. Circles: Analytical\nprediction [8] for a tesselation ratio of\n0.03, boxes: Prediction by sim telarray\nsimulation (α = 0.13). The difference in\nthe steepness of the curve is caused by\nthe difference in tesselation ratio.\n\nThe TAIGA IACTs are composed of 30 spherical mirror tiles (60 cm ø ) on a 4.75m ø\nDavies-Cotton dish. To compare our results to a semi-analytical model prediction, one must\ntake note of the tesselation ratio, the ratio between the diameters of the individual mirror tile\n(dtile) and the main dish (Ddish) :\n\nα =\ndtile\nDdish\n\n(1)\n\nThe relative size of the individual mirror tiles is important since larger tiles contribute larger\nabberation effects due to their spherical nature. TAIGA’s IACTs are designed with a tesselation\nratio of 0.13.\n\nFigure 1 shows the image of the point source at different off-axis angles in the camera layer.\nThe PSF is significantly smaller than a single camera pixel even at an off-axis angle of 5◦ at\nthe assumption of perfect mirror alignment. The star-like shape of the spot is an effect of the\noutermost mirror tiles.\n\nThe impact of the tesselation ratio on the PSF can be seen in Figure 2. The analytical\nprediction by Schliesser and Mirzoyan[8] assumes a tesselation ratio of 0.03, meaning much\nsmaller mirror tiles as compared to the TAIGA telescopes (α = 0.13). The overall shape is\ncloser to the shape of the main dish, with less abberation effects from the rims of the individual\ntiles.\n\nFor small angles, the minimal PSF of the TAIGA IACTs is not as good as expected from\nthe analytical predictions. With increasing angle however the TAIGA design shows a better\nPSF than predicted from the 0.03 model. This is an expected and desired effect since TAIGA\nIACTs are telescopes with a large field of view, so faithful imaging at large off-axis angles is\nmore important than reaching zero at the very center. Even at 5 ◦ off-axis, the TAIGA PSF\nstays considerably smaller than the radius of the camera pixels (0.19◦), leaving enough room\nfor the deviations brought by a real setup.\n\nSingle mirror PSF measurement The MC results shown in Figure 1 represent the minimal\nPSF possible under the assumption of perfect mirrors. Real mirrors have inaccuracies which will\nadd up and increase the overall PSF of the telescopes. To get an estimate on these inaccuracies,\nthe PSFs of several mirror tiles have been measured individually by placing a point-like light\n\n4 Magellan Workshop 2016\n\nMAIKE KUNNAS et al.\n\n146 Magellan Workshop 2016\n\n\n\nFigure 3: Scematic view of the single-mirror PSF measurement setup. To the left is the mirror,\nto the right the light source and screen close to the focal point.\n\nsource in the focal distance of the mirror and determining the angular size of the reflected spot\n(see Figure 3). For a perfect mirror, this spot should have the exact same dimensions as the\nsource, with any spread being the contribution of deviations from a perfect spherical shape.\n\n 0\n\n 0.2\n\n 0.4\n\n 0.6\n\n 0.8\n\n 1\n\n 0  0.01  0.02  0.03  0.04  0.05  0.06\n\nIn\nte\n\nns\nity\n\n [a\nrb\n\n. u\nni\n\nts\n]\n\ndistance from spot center [degr]\n\nTAIGA single mirror PSF measurement\n\n90%\n\n68%\n\nFull dish 68%\n\nFigure 4: Example for the cumulative\nintensity distribution of a point source\nreflected by a single TAIGA mirror.\nMarked are the 68% (1 σ) and 90 %\ncontainment radius as well as the MC-\ndetermined radius of the minimal PSF\nof the whole dish. It can be seen that\nthe single-mirror PSF is smaller than\nthe full dish PSF by about a factor of 2.\n\nSince a spherical mirror reflects a light source back onto itself when the source is put at the\nexact focal point, the measurements are done in a slight off-axis angle (<2◦). 28 TAIGA mirrors\nhave been measured in this procedure with all mirrors showing a PSF significantly smaller than\nthat of the full dish (for an example, see figure 4) while not varying much between each other.\nTherefore, the chosen mirrors are suitable for use in the TAIGA IACTs.\n\n3.2 Gamma Hadron Separation\n\nThe TAIGA IACTs are meant to improve the gamma hadron separation of the TAIGA array,\nespecially at its energy threshold at about 10-50 TeV[9]. To understand the range of improve-\nment, we first evaluate how powerful the IACTs are on their own. Since shower energy, impact\nparameter and direction are derived from the timing array’s measurements, we can focus our\nanalysis of the IACT images on the primary.\n\nThe parameter with the greatest dependence on the nature of the shower primary is the\nimage width, calculated via the second moment of the amplitude distribution after image clean-\n\nMagellan Workshop 2016 5\n\nSIMULATION OF IMAGING AIR SHOWER CHERENKOV TELESCOPES AS PART OF THE . . .\n\nMagellan Workshop 2016 147\n\n\n\nwidth[deg]\n0 0.1 0.2 0.3 0.4 0.5 0.6\n\nfr\nac\n\nti\no\n\nn\n o\n\nf \nev\n\nen\nts\n\n0\n\n0.01\n\n0.02\n\n0.03\n\n0.04\n\n0.05\nUnscaled width distribution\n\ngamma\n\nproton\n\nwidth[deg]\n0 0.1 0.2 0.3 0.4 0.5 0.6\n\nfr\nac\n\nti\no\n\nn\n o\n\nf \nev\n\nen\nts\n\n, q\nu\n\nal\nit\n\ny \nfa\n\nct\no\n\nr\n\n0\n\n0.2\n\n0.4\n\n0.6\n\n0.8\n\n1\n\n1.2\n\n1.4\n\n1.6\n\n1.8\n\n2\n\n2.2\nUnscaled width distr., cumul.\n\ngamma\n\nproton\n\nquality factor\n\nFigure 5: Raw image width distribution for zenith showers with EEAS = 10-50 TeV . Dashed:\nGammas, dotted: Protons, Solid: Quality factor. Left: Differential distribution. The proton\ndistribution has a bigger mean and spread than the gamma distribution, but the two peaks still\noverlap much. Right: Cumulative distribution and Q for the width cut. If a cut is made on the\nraw data, the maximum Q does not reach 1.6.\n\ning. This second moment depends mainly on the overall camera PMT amplitude (’size’), impact\nparameter and the primary. The higher number of hadronic interactions in a hadron-induced\nshower lead to a broader transversal momentum distribution of the particles in the shower,\nwhich in turn increases the image width compared to gamma- or electron-induced showers as\nseen in figure 5. A cut on the image width can separate gamma from hadron events. The cut\nquality factor is defined by:\n\nQ =\nǫγ√\nǫp\n\n(2)\n\nwith\n\nǫi =\nni(w < wcut)\n\nni\n, ǫi ∈ (γ, p) (3)\n\nEven though the two distributions have different shapes, they still overlap strongly due to\nvariation in the impact parameter and image size. With a width cut on the raw data, only a\nquality factor of Q=1.4 is reached. In order to improve the quality factor, each image width\nis scaled to the expectancy value for the respective image size, impact parameter and zenith\nangle. An exclusive gamma-only MC event data set was used to generate a lookup-table which\ncontains the width expectancy values.\n\nIn the final experiment, the selection of the correct lookup-table entry will be done with core\ndistance and zenith angle determined by the HiSCORE timing array. To give a first estimate\nof the final separation quality, a Toy core simulation was performed (see Figure 6). To emulate\nreconstruction by HiSCORE, the core position which is known from the MC is randomised with\nthe HiSCORE core position resolution, and the resulting value is used as the ’reconstructed’\ncore position when selecting the lookup table entry. This cuts on computing time since actually\nsimulating the showers in sim score is not necessary.\n\nThe value after scaling is called the Hybrid Scaled Width (HSCW):\n\n6 Magellan Workshop 2016\n\nMAIKE KUNNAS et al.\n\n148 Magellan Workshop 2016\n\n\n\nhscw\n0 0.5 1 1.5 2 2.5\n\nfr\nac\n\nti\no\n\nn\n o\n\nf \nev\n\nen\nts\n\n0\n\n0.01\n\n0.02\n\n0.03\n\n0.04\n\n0.05\nScaled width distribution\n\ntoy core gamma\n\ntoy core proton\n\nhscw\n0 0.5 1 1.5 2 2.5\n\nfr\nac\n\nti\no\n\nn\n o\n\nf \nev\n\nen\nts\n\n, q\nu\n\nal\nit\n\ny \nfa\n\nct\no\n\nr\n\n0\n\n0.2\n\n0.4\n\n0.6\n\n0.8\n\n1\n\n1.2\n\n1.4\n\n1.6\n\n1.8\n\n2\n\n2.2\nScaled width distr., cumul.\n\ntoy core gamma\n\ntoy core proton\n\ntoy core quality factor\n\nFigure 6: HSCW distribution for zenith showers with EEAS= 10-50 TeV. Dashed: Gammas,\ndotted: Protons, Solid: Quality factor. Left: Differential distribution. Compared to the un-\nscaled plot, the gamma and the proton distribution have moved apart significantly. Right:\nCumulative distribution and Q for the width cut. If a cut is made on the HSCW, the maximum\nQ is about 2.1.\n\nHSCW =\nwtel\n\nwγ\nMC(darray, sizetel)\n\n(4)\n\nScaling the width makes the two distributions move apart, and now a cut on the HSCW\nyields us Q∼1.9, which is not only better than an unscaled cut, but also better than the\nHiSCORE-only separation at those. The timing array can achieve Q ∼ 1 at the threshold and\nonly starts to approach 2 at a hundred TeV.[10][9]\n\nThe blurring of the core position lowers the separation quality only little compared to the\ndirect use of the MC core position (∼ 1.9) even though HiSCORE’s core resolution is strongly\nenergy-dependent near the energy threshold.\n\n4 Conclusion and Outlook\n\nTAIGA’s combination of IACTs with timing arrays is a valid method of obtaining the large\nareas needed to observe ultra high energy gamma rays. The design of the TAIGA telescopes will\nprovide suitable shower images as tested both by MC simulation and measurement of prototype\nparts. Also, adding IACTs to a timing array will provide a possibility of rejecting the hadronic\nbackground with a quality factor of 2.1 near the energy threshold, where the timing array alone\ncould not make a distinction.\n\nThe studies so far are not yet a full hybrid reconstruction. In the full reconstruction, the\ndirectional information stored in the IACT images will also be taken into account to improve\nthe resolution of shower core position and incidence angle. Also the timing array signals do\ncontain information on the nature of the primary in the signal rise time, so for higher energies\nthe gamma hadron separation quality will be much better than 2.\n\nFurthermore, the IACTs can be used to reduce the energy threshold.The timing array needs\n4 stations with signal for a proper reconstruction, but a shower can also be reconstructed with\nthe IACT image and one station only. The combination also makes it possible to indirectly\nincrease the field of view of the telescopes. So far, shower images that are cut off by the edge\n\nMagellan Workshop 2016 7\n\nSIMULATION OF IMAGING AIR SHOWER CHERENKOV TELESCOPES AS PART OF THE . . .\n\nMagellan Workshop 2016 149\n\n\n\nof the camera are rejected because they are incomplete, but the shower information from the\ntiming array might allow to interpolate the shape of the full shower.\n\n5 Acknowledgments\n\nWe acknowledge the support of the Russian Federation Ministry of Education and Science\n(agreements N 14.B25.31.0010, N2014/15, project 1366, zadanie N 3.889.2014/K), the Russian\nFoundation for Basic Research (grants 13-02-00214, 15-02-10005, 13-02-12095), The Helmholtz\nAssociation (grant HRJRG-303), and the Deutsche Forschungsgemeinschaft(grant TL 51-3).\n\nReferences\n[1] M.Tluczykont et al, Connecting neutrino Astrophysics to Multi-TeV to PeV gamma-ray astronomy with\n\nTAIGA these proceedings (2016)\n\n[2] Hillas A M 1985 Cerenkov light images of EAS produced by primary gamma Int. Cosm. R. Conf. 3 4458.\n\n[3] D. Heck et al, Report FZKA 6019 (1998), available from http://www-\nik.fzk.de/corsika/physics description/corsika phys.html\n\n[4] N. N. Kalmykov, S. S. Ostapchenko, and A. I. Pavlov Nucl. Phys. B (Proc. Suppl.) 52B 17 (1997)\n\n[5] H. Fesefeldt, Report PITHA-85/02 (1985)\n\n[6] M. Tluczykont et al, The HiSCORE concept for gamma-ray and cosmic-ray astrophysics beyond 10 TeV\nAstropart. Phys. 56 42-53 (2014) [1403.5688].\n\n[7] K. Bernlöhr Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim telarray,\nAstropart. Phys. 149-158 (2008)\n\n[8] A. Schliesser and R. Mirzoyan, Wide-field prime-focus imaging atmospheric Cherenkov telescopes: A sys-\ntematic study, Astrop. Phys.24 382-390 (2005) [astro-ph/0507617]\n\n[9] M. Tluczykont et al, Towards gamma-ray astronomy with timing arrays in proceedings of ECRS (2014)\n\n[10] D. Hampf, M. Tluczykont and D. Horns, Event reconstruction techniques for the wide-angle air Cherenkov\ndetector HiSCORE Nucl. Inst. Meth. in Phys. Res. A 137-146 (2012/13) [1302.3957]\n\n8 Magellan Workshop 2016\n\nMAIKE KUNNAS et al.\n\n150 Magellan Workshop 2016\n\n\n\nRecent Results from the MAGIC Telescopes\n\nDorit Eisenacher Glawion1, for the MAGIC Collaboration2\n\n1Universität Würzburg, Institut für Theoretische Physik und Astrophysik, Emil-Fischer-Straße\n31, 97074 Würzburg, Germany\n2https://magic.mpp.mpg.de/\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/24\n\nMAGIC consists of two imaging air Cherenkov telescopes located at La Palma, Spain.\nSince 12 years, the telescopes are performing scientific observations of gamma rays with\nenergies between 35GeV and 50TeV.\n\nScientific highlights from MAGIC concerning both galactic and extragalactic sources in\nthe context of multi-messenger astronomy are presented. Among these, measurements of\nthe TeV emission from Crab observations are shown. Also, the ultra-fast variability of\nIC 310, which challenges jet emission models in AGNs, is discussed. Furthermore, the\ndetection of very-high-energy gamma-ray emission from two active galaxies, B0 218+357\nand PKS1441+25, located at redshift of ∼0.94 is presented.\n\n1 Introduction\n\nCosmic rays were discovered more than one hundred years ago by Victor Hess. Still, there is\nan increasing research field connected to their nature, their origin, and their propagation. Due\nto the deflection of the charged particles of the cosmic rays, e.g., by the magnetic fields of the\ninterstellar and the intergalactic medium they reach the Earth mostly isotropic.\n\nInteractions of cosmic rays among themselves and/or with photon fields result in the pro-\nduction of pions. These decay into neutral and charged muons, which themselves decay into\ngamma rays and neutrinos, respectively. This means that the detection of gamma rays and\nneutrinos from point sources can provide an unambiguous signature of the origin of the cosmic\nrays. While the detection of neutrinos is more challenging, a large and rising number of sources\nof gamma rays have already been discovered. While in the high-energy gamma-ray regime\n20MeV . E . 100GeV, satellite-based instruments such as the Fermi- LAT instrument are\nused, gamma-rays at higher energies (100GeV . E . 100TeV) are detected with ground-based\ntelescopes based on the Imaging Air Cherenkov Technique.\n\nThe Major Atmospheric Gamma Imaging Cherenkov Telescope (MAGIC) is a system of two\nImaging Air Cherenkov Telescopes (IACT) with a mirror dish diameter of 17m each, separated\nby a distance of 85m. It is located at the Observatorio del Roque de los Muchachos on the\nCanary island of La Palma, Spain, at an altitude of ∼2200m above sea level. The construction\nof the first telescope, MAGIC-I, was completed in 2003 and it performs scientific observations\nsince 2004. In 2009, the second telescope, MAGIC-II, joined in. Since then, the two telescopes\nare working together as a stereoscopic system which allows an improved performance in terms\nof sensitivity and angular resolution, among other things. The energy range covered by MAGIC\noverlaps with the accessible range of Fermi-LAT at low energies, starting at 50GeV (standard\n\nMagellan Workshop 2016 1Magellan Workshop 2016 151\n\n\n\ntrigger) and reaching up to 50TeV. Some observations are also performed with a new digitalized\ntrigger technique and allows for measurements even below 50GeV [1].\n\nThe IACT technique is based on the production of air showers by highly energetic particles\nin the Earth’s atmosphere. If a gamma-ray photon with more than 10MeV of energy enters the\natmosphere the production of an electron-positron pair dominates the absorption. Due to an\ninteraction with molecules in the air, secondary gamma rays are produced by Bremsstrahlung.\nThese secondaries again may produce e+e− pairs. The resulting electromagnetic cascade con-\ntinues until the average energy reaches the value at which the losses due to ionization and\nBremsstrahlung are equal. The secondary electrons in those showers, if energetic enough, i.e.,\nwhen moving faster than the speed of light in the atmosphere, can activate the emission of\nCherenkov photons in a cone with a small opening angle. Inside the Cherenkov light pool,\nmirror telescopes can be placed in order to measure the Cherenkov light. The spectrum of this\nlight is mostly in the optical and ultra-violet range. The faint emission can be measured with\nphoto-multipliers as also done with MAGIC. Due to a short duration of the Cherenkov pulses,\na fast readout system needs to be connected with the telescope. Besides showers introduced\nby gamma rays, showers induced by hadrons (e.g., protons) exist. Those showers form the\ndominating background and hence are the limiting factor for ground-based gamma-ray astron-\nomy. Typically, one gamma-ray event observed by an IACT telescope is hidden behind 1000\nbackground events.\n\nWith this technique, MAGIC is able to achieve a sensitivity of 0.6% of the Crab Nebula\nflux for a 5 σ detection in 50 h of observation in the best range at energies of E ∼ 400GeV.\nThe angular and energy resolution, both dependent on the energy, is 0.1◦ − 0.07◦ and 15–20%,\nrespectively. Details about the performance of MAGIC can be found in [2].\n\nThe scientific focus of MAGIC is set on the investigation of the gamma-ray emission from\ngalactic objects of various types as well as on active galactic nuclei and gamma-ray bursts as\nextragalactic objects. Follow-up observations of gamma-ray bursts by MAGIC are reported in\n[3]. Furthermore, MAGIC concentrates on the indirect measurements of dark matter [4, 5, 6] as\nwell as on setting constrains on the cosmic rays [7], the extragalactic background light [8], the\nLorentz invariance violation [9], and the intergalactic magnetic field. In this paper, only results\nfrom galactic objects, active galactic nuclei, and follow-up observations of neutrino events are\nbeing reviewed.\n\n2 Galactic science\n\nMAGIC observes a variety of galactic objects which are mostly in connection with the end\nstages of stars, e.g., novae, supernova remnants, pulsars, and pulsar wind nebulae.\n\n2.1 Crab Nebula and its pulsar\n\nThe Crab Nebula is the standard candle for the very-high-energy astrophysics at energies E >\n50GeV. Although it is known to be a TeV emitter for a long time this pulsar wind nebula\nis still of interest for current research. Recent MAGIC observations between October 2009\nand April 2011 of 69 h revealed a spectrum ranging over the entire accessible energy range\nover three decades in energy [10]. Together with Fermi-LAT data, these yielded the most\n\nprecise measurement of the inverse Compton peak of 53\n+3stat+31syst\n−3stat−13syst\n\nGeV that is dominated by\nsystematic effects. Only a modified log-parabola function with an exponent of 2.5 instead of\n\n2 Magellan Workshop 2016\n\nDORIT EISENACHER GLAWION\n\n152 Magellan Workshop 2016\n\n\n\n2 fits the obtained spectrum. It was found that a constant magnetic field model does not\nreproduce the broadness of the inverse Compton peak. Most probably, this suggests that the\nassumption of homogeneity of the magnet field inside the nebula is wrong. Three state-of-the-\nart models were applied to the spectral energy distribution. A time-dependent 1D spectral\nmodel is able to match the data with a magnetic field of 80µG. However, it fails to describe\nthe data when including the morphology of the nebula at higher frequencies.\n\nInside the nebula one finds the pulsar. The first detection of its pulsed gamma-ray emission\nabove 25GeV by a ground-based instrument was already achieved in 2008 by MAGIC [11].\nAfter the accumulation of 320h of observations, a spectrum between 70GeV and 1.5TeV was\nmeasured [12]. The pulse profile shows two narrow peaks which are synchronized with the ones\nmeasured, e.g., by Fermi-LAT in the lower energy gamma-ray range. The spectra of pulse I and\nII were found to follow different simple power-law indices, 3.2± 0.4 and 2.9± 0.2, respectively.\nThe pulsed TeV emission can not be produced by synchrotron-curvature radiation. Instead, the\ninverse Compton process plays a dominating role at energies above 50GeV. Furthermore, all\ncurrent existing models fail to reproduce the observed narrowness of the peaks. The observation\nposes strong constrains on the Lorentz factors of the electrons. A parent population of electrons\nwith γ > 5× 106 is needed. This suggests an inverse Compton scattering of low energy photons\nas the emission and that the gamma-ray production region is located near by the light cylinder.\n\n2.2 The supernova remnant W51C\n\nIn 2010 and 2011, MAGIC collected more than 50h of data from the region of W51 [13]. This\ncomplex field hosts the supernova remnant W51C which interacts with molecular clouds in the\nstar forming region of W51B. Observations at other frequencies suggest a possible association\nof a pulsar wind nebula with the remnant. Gamma rays were detected from W51 but it was not\nclear if this emission is originating from the pulsar wind nebula, the supernova remnant shell,\nand/or the molecular cloud. The spatial resolution of the measurements by MAGIC helped\nto pinpoint the location of the gamma-ray emission. The skymaps show an extension at high\nenergies towards the pulsar. However, it was not possible to establish it as an independent\nsource. The contribution from the pulsar wind nebula was estimated to be ∼17% of the flux,\nbut the shocked cloud region is dominant at all energies.\n\nFurthermore, the measured spectrum follows the high-energy points measured with Fermi-\nLAT and ranges up to ∼ 5TeV with a simple-power law function. Modeling of the spectral\nenergy distribution is consistent with a hadronic origin of the gamma-ray emission. It was found\nthat an ongoing acceleration of ions in the interaction zone between the supernova remnant and\nthe cloud can explain the observations.\n\n2.3 Novae and dwarf novae\n\nThe Fermi-LAT telescope detected gamma-rays at GeV energies from a few novae in the last\nyears. This emission can be interpreted in terms of the inverse Compton process of electrons\nwhich are accelerated in shocks of novae. In the same manner, protons might be accelerated\nto much higher energies under the same condition. This could produce a second gamma-ray\nemission component in the spectra possibly at TeV energies.\n\nFor this reason, MAGIC performed follow-up observations of novae and dwarf novae [14].\nOne example is the observation of the classical novae V339Del in August and September 2013.\nThese measurements were triggered by an outburst found in the optical and GeV band. MAGIC\n\nMagellan Workshop 2016 3\n\nRECENT RESULTS FROM THE MAGIC TELESCOPES\n\nMagellan Workshop 2016 153\n\n\n\ndid not detect a significant signal, but upper limits to the spectral energy distribution together\nwith the data points from Fermi-LAT limit the ratio of the proton-to-electron luminosity to be\nLp . 0.15Le in theoretical models.\n\n3 Extragalactic science\n\nThe extragalactic gamma-ray sky currently counts ∼ 65 objects1. The parent population con-\nsists of radio-loud active galactic nuclei with jets pointing towards the earth. Those objects are\ncalled blazars and they are typically characterized by their strong variability in all wavelengths\nand on all time scales. The so-called Doppler boosting effect plays an important role for the\ninterpretation of the emission of these objects. However, a handful of radio galaxies for which\nthe angle between the jet-axis and the line-of-sight is larger were discovered as well.\n\n3.1 Black hole lightning from IC310\n\nIC 310 is an active galaxy located in the outskirts of the Perseus cluster of galaxies at a cosmo-\nlogical redshift of z = 0.0189. Originally, this object was classified as a head-tail radio galaxy,\nwhere the jets of the nucleus are being bend due to the movement of the galaxy through the\ncluster. However, studies of the multi-wavelength emission showed several properties similar to\nthose of blazars as well as radio galaxies.\n\nDuring an extensive multi-wavelength program in late 2012, MAGIC detected an exception-\nally bright TeV flare from IC 310 on one night reaching an average flux level of up to one Crab\nabove 1 TeV with a hard spectrum over two decades in energy [15]. The intra-night light curve\nshowed a series of strong outbursts with flux-doubling time scales as fast as a few minutes. The\nfast variability constrains the size of the gamma-ray emission regime to be smaller than 20%\nof the gravitational radius of its central black hole. Furthermore, very long baseline interfer-\nometry measurements in the radio band revealed a limit for the viewing angle of the active\ngalactic nucleus to be 10◦ . θ . 20◦. Thus, no strong Doppler boosting can be assumed in the\nexplanation of the gamma-ray production mechanism. This challenges the standard model for\ngamma-ray emission of active galaxies being produced due to the acceleration of particles at\nshocks in the jets. It was found that a magnetospheric model similar to those used to interpret\nthe gamma-ray emission from pulsars is able to explain the phenomena. Instead of having a\nneutron star in the center of the magnetosphere, a rotating black hole exists. In this model, the\ngamma rays are being produced by electromagnetic cascades, acceleration inside vacuum gaps,\nemission via the inverse Compton, and curvature radiation processes.\n\n3.2 Gravitationally lensed blazar S3 0218+35\n\nS3 0218+35 is a blazar at a redshift of z = 0.944, the most distant and the only gravitationally\nlensed gamma-ray emitting object of the very-high-energy sky today. The lensing galaxy is a\nspiral galaxy and is located at the redshift of z = 0.68. In July 2014, Fermi-LAT observed\nan outburst from this objects which triggered follow-up observations with MAGIC. This flare\nhappened during full moon time where MAGIC interrupts observations. However, previous\nstudies from the Fermi team as well as of the radio emission showed that the gravitationally\nlensed signals splits into two components separated by 10-12 days. MAGIC observations were\n\n1http://tevcat2.uchicago.edu/\n\n4 Magellan Workshop 2016\n\nDORIT EISENACHER GLAWION\n\n154 Magellan Workshop 2016\n\n\n\nscheduled around the expected time of arrival of the trailing component. Thus, MAGIC detected\nthe object with a significance of more than 5 σ at ∼ 12 d after the flare seen by Fermi-LAT [16].\n\n3.3 Quasar PKS1441+25\n\nThe second most distant active galaxy in the very-high-energy range is PKS1441+25, a flat-\nspectrum radio quasar located at a redshift of z = 0.939. Similar to the detection of S3 0218+35,\nthe observations of PKS1441+25 were triggered by an outburst of the object measured with\nthe Fermi-LAT telescope in April 2015. MAGIC observed the object during April and May\n2015, resulting in the detection of a signal of 25.5 σ in an energy range of 40–250GeV and a\nvariability time scale of 6.5±1.9d [17]. In addition, many telescopes in various frequency bands\nperformed observations, resulting in an excellent coverage of the multi-wavelength spectral\nenergy distribution for the object at different flux states. All observations together suggest a\nsubdivision of the high state into two distinct flux states. The theoretical interpretation of the\nspectral energy distribution with an external Compton model yielded that the location of the\ngamma-ray emitting region during the period of high activity is located in the jet but outside\nthe broad-line region, while the emitting region during the low activity is partially inside the\nbroad-line region.\n\n4 Follow-up of neutrino events\n\nAs said before, the detection of neutrino emission from astronomical objects would certainly\ncontribute to the search of the origin of the cosmic rays. However, measuring a significant\nneutrino excess especially from a point-source is challenging due to the much smaller cross\nsection for neutrino interaction. The IceCube Collaboration reported on the detection of 37\nneutrino events between 30TeV and 2PeV, spread over both hemispheres and rejecting an\natmospheric origin at a 5.7 σ level [18]. However, most of these events have a large median\npositional uncertainty of 10◦ to 20◦. Thus, IACT instruments with an angular resolution of the\norder of ∼ 0.1◦ could provide follow-up gamma-ray observations for neutrino candidates and\ntransient neutrino events.\n\nFor this reason, IACT instruments, e.g., MAGIC have been receiving on-line triggers from\nthe IceCube Collaboration for a few years. A precedent case including the precursor of Ice-\nCube, AMANDA, is being reported in [19]. So far, no significant signal was found from such\nobservations but they are still crucial as they allow for performing of real-time multi-messenger\nastronomy.\n\n5 Summary\n\nThe last few years of scientific operation of the MAGIC telescopes were very successful, e.g.,\nresulting in the detection of the most energetic pulsed emission from an astronomical object,\nthe measurement of gamma rays from active galactic nuclei in our neighborhood or from the\nmiddle age of the universe, and ultra-fast variability giving new clues on particle acceleration.\n\nThe MAGIC team makes strong efforts in collaborating with many telescopes in all other\nfrequency ranges as well as the Cherenkov gamma-ray instruments VERITAS, H.E.S.S., FACT,\nand HAWC. In addition, MAGIC performs follow-up observations of gravitational wave as well\nas neutrino events.\n\nMagellan Workshop 2016 5\n\nRECENT RESULTS FROM THE MAGIC TELESCOPES\n\nMagellan Workshop 2016 155\n\n\n\nIn the next years, MAGIC will continue to observe the very-high-energy gamma-ray sky\nbridging the gap until the future Cherenkov Telescope Array, CTA, will start its operation. The\nbuilding of the first prototype of a 23m diameter Large Size Telescope for CTA has already\nstarted in 2015 right next to the MAGIC site.\n\nAcknowledgments\n\nWe would like to thank the Instituto de Astrof́ısica de Canarias for the excellent working\nconditions at the Observatorio del Roque de los Muchachos in La Palma. The financial support\nof the German BMBF and MPG, the Italian INFN and INAF, the Swiss National Fund SNF,\nthe ERDF under the Spanish MINECO (FPA2012-39502), and the Japanese JSPS and MEXT\nis gratefully acknowledged. This work was also supported by the Centro de Excelencia Severo\nOchoa SEV-2012-0234, CPAN CSD2007-00042, and MultiDark CSD2009-00064 projects of the\nSpanish Consolider-Ingenio 2010 programme, by grant 268740 of the Academy of Finland, by\nthe Croatian Science Foundation (HrZZ) Project 09/176 and the University of Rijeka Project\n13.12.1.3.02, by the DFG Collaborative Research Centers SFB823/C4 and SFB876/C3, and by\nthe Polish MNiSzW grant 745/N-HESS-MAGIC/2010/0.\n\nReferences\n[1] F. Dazzi et al., PoS ICRC2015 984, (2015).\n\n[2] J. Aleksić et al., APh 72 76 (2016).\n\n[3] A. Carosi et al., PoS ICRC2015 809, (2015).\n\n[4] J. Aleksić et al., ApJ 710 634 (2010).\n\n[5] J. Aleksić et al., JCAP 02 008 (2014).\n\n[6] M.L. Ahnen et al., JCAP 02 039 (2016).\n\n[7] M.L. Ahnen et al., A&A 589A 33 (2016).\n\n[8] M.L. Ahnen et al., arXiv:astro-ph/1602.05239 (2016).\n\n[9] MAGIC Collaboration et al., Phys. Lett. B 668 253 (2008).\n\n[10] J. Aleksić et al., JHEAp 5 30 (2015).\n\n[11] E. Aliu et al., Science 322 1221 (2008).\n\n[12] S. Ansoldi et al., A&A 585A 133 (2016).\n\n[13] J. Aleksić et al., A&A 541A 13 (2012).\n\n[14] M.L. Ahnen et al., A&A 582A 67 (2015).\n\n[15] J. Aleksić et al., Science 346 1080 (2014).\n\n[16] J. Sitarek et al., PoS ICRC2015 825, (2015).\n\n[17] M.L. Ahnen et al., ApJ 815L 23 (2015).\n\n[18] M.G. Aartsen et al., Phys. Rev. Lett. 113 101101 (2014).\n\n[19] M. Ackermann et al., arXiv:astro-ph/0709.2640 (2007).\n\n6 Magellan Workshop 2016\n\nDORIT EISENACHER GLAWION\n\n156 Magellan Workshop 2016\n\n\n\nNeutrino Geoscience\n\nLivia Ludhova\n\nIKP-2 Forschungzentrum Jülich, 52428 Jülich, Germany\nRWTH Aachen University, 52062 Aachen, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/7\n\nNeutrino geoscience is a newly born interdisciplinary field having as its main aim determi-\nnation of the Earth’s radiogenic heat through measurement of geoneutrinos: antineutrinos\nreleased in decays of long-lived radioactive elements inside the Earth. In fact, such mea-\nsurements are a unique direct way how to pin-down this key element for many geophysical\nand geochemical Earth’s models. The large–volume liquid scintillator detectors, originally\nbuilt to measure neutrinos or anti-neutrinos from other sources, are capable to detect\ngeoneutrinos, as it was demonstrated by KamLAND (Japan) and Borexino (Italy) experi-\nments. Several future projects as SNO+ or JUNO have geoneutrino measurements among\ntheir scientific goals. This work covers the status-of-art of this new field, summarising its\npotential in terms of geoscience, the status of existing experimental results, and future\nprospects.\n\nFigure 1: Energy spectrum of geoneutrinos. The dashed vertical line shows the energy threshold\nof the IBD interaction: currently the only detection method for geoneutrinos.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 157\n\n\n\nFigure 2: Schematic visualisation of the relations among the abundances and distributions\nof radioactive elements inside the Earth, the geoneutrino flux, and the radiogenic heat, the\nultimate goal of geoneutrino measurements.\n\n1 What are geoneutrinos and the main goal\n\nGeoneutrinos are electron-flavour antineutrinos emitted in the β decays of long-lived radioactive\nelements, called also the heat producing elements (HPE). Geoneutrinos are emitted along the\ndecay chains of 238U and 232Th and in the 40K decay:\n\n238U →206 Pb + 8α+ 8e− + 6ν̄e + 51.7 MeV, (1)\n\n232Th →208 Pb + 6α+ 4e− + 4ν̄e + 42.7 MeV, (2)\n\n40K →40 Ca + e− + ν̄e + 1.31 MeV (BR 89.3%). (3)\n\nGeoneutrino spectrum is shown in Fig. 1. Geoneutrinos are detected by the so called Inverse\nBeta Decay (IBD) interaction:\n\nν̄e + p → e+ + n. (4)\n\nDue to the 1.8MeV kinematic threshold of this reaction, only the high-energy tail of 238U and\n232Th geoneutrinos can be detected, while all 40K geoneutrinos are below the threshold.\n\nThe main aim of geoneutrino studies is to determine the Earth’s radiogenic heat, espe-\ncially the unknown contribution from the mantle. The mantle composition is quite unknown\nwith respect to the better-known crustal composition. Knowing the mass/abundances of HPE,\nthe radiogenic heat is directly determined. The geoneutrino studies are, however, complicated\nthrough an unknown distribution of HPE, on which depends both the geoneutrino signal pre-\ndiction as well as the final interpretation of the measured geoneutrino flux, see Fig. 2. Due to\nthis, a close collaboration between neutrino physicists and geoscientists is strongly required in\norder to exploit the potential of geoneutrinos to its maximum.\n\n2 Magellan Workshop 2016\n\nLIVIA LUDHOVA\n\n158 Magellan Workshop 2016\n\n\n\nFigure 3: A schematic profile of the Earth structure (from\nhttp://www.homepages.ucl.ac.uk/ ucfbdxa/resint.htm).\n\n2 The Earth and GeoSciences\n\nThe Earth was created in the process of accretion from undifferentiated material, to which\nchondritic meteorites are believed to be the closest in composition and structure. The bodies\nwith a sufficient mass undergo the process of differentiation, e. g., a transformation from an\nhomogeneous object into a body with a layered structure. The metallic core (3500km radius)\nwas the first to separate from the silicate primordial mantle, which further differentiated into\nthe current mantle (3000km thickness) and the crust (5 to 75km). The Fe–Ni metallic core\nwith up to ∼10% admixture of lighter elements, has a temperatures range from 4100 to 5800K.\nIts central part, inner core with the radius ∼1300km is solid due to high pressure. The 2200 km\nthick outer core is liquid and has a key role in the geo–dynamo process generating the Earth’s\nmagnetic field. The D\n\n′′\nlayer is a core–mantle boundary, a 200km thick seismic discontinuity of\n\nunclear origin. The lower mantle (2000 km) with a temperature gradient from 600 to 3700K is\nsolid, but viscose on long time scales. It is involved in the convection driving the movement of\ntectonic plates with a speed of a few centimetres per year. A transition zone in the depth of 400\n- 600km is a seismic discontinuity due to mineral recrystallisation. The upper mantle contains\nviscose asthenosphere on which are floating the lithospheric tectonic plates. These comprise\nthe uppermost, rigid part of the mantle and the crust of two types: oceanic and continental.\nThe continental crust (30 km average thickness) has the most complex history being the most\ndifferentiated and heterogeneous, consisting of igneous, metamorphic, and sedimentary rocks.\nThe oceanic crust (5 - 10 km) is created along the mid–oceanic ridges, where the basaltic magma\ndifferentiates from the partially melting mantle. A schematic profile of the Earth structure can\nbe found in Fig. 3.\n\nMagellan Workshop 2016 3\n\nNEUTRINO GEOSCIENCE\n\nMagellan Workshop 2016 159\n\n\n\nThe HPE are so called refractory, lithophile elements: they bound to the lighter partially\nmelted liquid, leaving behind the HPE-depleted solid and denser residuum. Due to this mech-\nanism, the HPE are strongly concentrated in the most complex continental crust (at the level\nof few ppm in average) and present in sub-ppm concentrations in the oceanic crust. The HPE\nconcentration in the mantle is suppressed by few orders of magnitude and largely unknown.\nDue to the chemical affinity to silicates, no HPE are expected to be present in the metallic core.\n\nHow do we get information about the deep Earth? Seismology studies the propagation of the\nS and P seismic waves, providing the velocity and density profiles of the Earth. Geochemistry\nstudies the chemical composition of the Earth. The depth of accessible rock samples is limited\nto the upper mantle. The global composition of the Bulk Silicate Earth (BSE) is derived\nthrough geochemical modelling, considering the correlations in isotopic abundances in the solar\nphotosphere and in meteorites, as well as composition of the present day rocks. These BSE\nmodels describe the composition of the Primitive Mantle, the Earth composition after the core\nseparation and before the crust-mantle differentiation. The estimates of the composition of the\npresent-day mantle can be derived as a difference between the mass abundances predicted by\nthe BSE models in the Primitive Mantle and those observed in the present crust. In this way,\nthe predictions of the U and Th mass abundances in the mantle are made. An overview of\ndifferent BSE models can be found for example in Šrámek et al. [1].\n\nThe Earth’s surface heat flux is estimated based on the measurements of temperature gradi-\nents along several thousands of drill holes around the globe. The most recent evaluation of these\ndata leads to the prediction of 47± 2TW predicted by Davies and Davies (2010) [2], consistent\nwith the estimation of Jaupart et al. (2007) [3]. The relative contribution of the radiogenic heat\nfrom radioactive decays to this flux is not known and this is the key information which can be\npinned down by the geo-neutrino measurements. The geochemical, cosmochemical, and geody-\nnamical models predict the radiogenic heat of 20 ± 4, 11 ± 2, and 33 ± 3TW, respectively [1].\nThe crustal radiogenic power was recently evaluated by Huang et al. [4] as 6.8+1.4\n\n−1.1TW. By\nsubtracting this contribution from the total radiogenic heat predicted by different BSE models,\nthe mantle radiogenic power can be as little as 3TW and as much as 23TW. To determine this\nmantle contribution is one of the main goals and potentials of Neutrino Geoscience.\n\n3 Geoneutrino detection\n\nGeoneutrinos are detected by the large–volume liquid scintillator detectors placed in under-\nground laboratories, in order to shield them from the cosmic radiation. The target for the IBD\ndetection interaction shown in Eq. 4 are hydrogen nuclei from the hydrocarbon molecules of\norganic liquid scintillator. In this process, a positron and a neutron are emitted as reaction\nproducts. The positron promptly comes to rest and annihilates emitting two 511keV γ-rays,\nyielding a prompt event, with a visible energy Eprompt, directly correlated with the incident\nantineutrino energy Eν̄e :\n\nEprompt = Eν̄e − 0.784 MeV. (5)\n\nThe emitted neutron keeps initially the information about the ν̄e direction, but is typically\ncaptured on protons only after quite a long time (τ = 200 - 250µs, depending on scintillator),\nduring which the directionality memory is lost in many scattering collisions. When the ther-\nmalised neutron is captured on proton, it gives a typical 2.22MeV de-excitation γ-ray, which\nprovides a coincident delayed event. The pairs of time and spatial coincidences between the\n\n4 Magellan Workshop 2016\n\nLIVIA LUDHOVA\n\n160 Magellan Workshop 2016\n\n\n\nFigure 4: KamLAND prompt energy spectrum of ν̄e events in the geoneutrino energy region.\nData together with the best-fit background and geo contributions are shown. Taken from [8].\n\nprompt and the delayed signals offer a clean signature of ν̄e interactions, very different from\nthe νe scattering process used in the neutrino detection.\n\nFor a ∼3MeV antineutrino, the oscillation length is of ∼100km, small with respect to the\nEarth’s radius of ∼6371km: the effect of the neutrino oscillation to the total neutrino flux is\nwell averaged. Considering also the local matter effects [5], the net effect of flavour oscillations\nduring the geo-neutrino propagation through the Earth is the average electron flavour survival\nprobability of ∼0.55 with a very small spectral distortion, negligible for the precision of the\ncurrent geo-neutrino experiments.\n\nTypically, for the experimental sites built at the continental crust, about half of the total\ngeoneutrino signal comes from the local crust in the area of few hundreds of kilometers around\nthe detector [4]. In order to extract the mantle contribution from the measured geoneutrino\nsignal, it is necessary to be able to subtract the crustal contribution. This means, that the local\ngeology in the area of the experiment has to be known.\n\n4 Latest geoneutrino measurements\n\nToday, only two experiments succeeded to measure geoneutrinos: KamLAND [6, 7, 8] in\nKamioka mine in Japan and Borexino [9, 10, 11] in Gran Sasso underground laboratory in\nItaly. KamLAND is a 1 kton liquid scintillator detector dedicated to the measurement of re-\nactor antineutrinos, which provided one of the first proves of the existence of the phenomenon\nof neutrino oscillations and the most precise measurement of the ∆m2\n\n12 mass splitting [12].\nBorexino is 280 ton liquid scintillator detector, originally built to measure low-energy (below\n1MeV) solar neutrinos by means of elastic scattering off electrons. In contrast to IBD interac-\n\nMagellan Workshop 2016 5\n\nNEUTRINO GEOSCIENCE\n\nMagellan Workshop 2016 161\n\n\n\nFigure 5: Prompt light yield spectrum in photoelectrons (p.e.) of 77 antineutrino candidates\nmeasured by Borexino and the best fit. 1MeV corresponds to ∼500p.e. Taken from [11].\n\ntions (delayed coincidences), the measurement of singles puts more stringent requirements on\nthe detector radio-purity. Borexino detector reached extremely high levels of radio-purity and\nproduced unique results in solar neutrino spectroscopy [13]. Borexino geoneutrino measure-\nment is almost free of non-antineutrino background components, as random coincidences, (α,\nn) interactions and cosmogenic events. In addition, no nuclear power plants are in operation in\nItaly, further reducing the background for geoneutrino measurement.\n\nThe latest KamLAND result, 116+28\n−27 geoneutrinos detected with 4.9× 1032 target-proton ×\n\nyear exposure, is from 2013 [8], including the period of low reactor antineutrino background after\nthe April 2011 Fukushima disaster. The measured signal is in agreement with expectations,\nslightly disfavouring the geodynamical BSE models. The contribution of the local crust to the\ntotal geoneutrino signal was studied in [5, 14].\n\nThe new Borexino update from 2015 [11] is shown in Fig. 5. Within the exposure of (5.5±\n0.3)×1031 target-proton × year, 23.7+6.5\n\n−5.7(stat)\n+0.9\n−0.6(sys) geoneutrino events have been detected.\n\nThe null observation of geoneutrinos has a probability of 3.6× 10−9 (5.9σ). The contribution\nof the local crust to the total signal was studied in [15]. A geoneutrino signal from the mantle\nis confirmed at 98% confidence level. The radiogenic heat production for U and Th from the\npresent best-fit result is restricted to the range 23 to 36 TW, taking into account the uncertainty\non the distribution of HPE inside the Earth.\n\n5 Future prospects\n\nIn the near future, a possible update is expected from KamLAND, including more data with\nlow reactor antineutrino background. Borexino geoneutrino data-set will naturally end with the\n\n6 Magellan Workshop 2016\n\nLIVIA LUDHOVA\n\n162 Magellan Workshop 2016\n\n\n\nFigure 6: Example of a toy Monte Carlo for a possible 1-year geoneutrino measurement of\nJUNO. The different spectral components are shown as they result from the fit; black line\nshows the total sum for the best fit. The geoneutrino signal with Th/U fixed to chondritic\nratio is shown in red. The following colour code applies to the backgrounds: orange (reactor\nantineutrinos), green (9Li - 8He), blue (accidental), small magenta (α, n). Taken from [17].\n\nstart of the SOX project at the end of 2017. Placing the strong 144Ce-144Pr antineutrino source\nbelow the detector (in order to search for a hypothetical 1 eV2 sterile neutrino), will make it\nimpossible to measure geoneutrinos with its low rate of about 1 event every 2-3 months.\n\nFrom the future projects, the SNO+ experiment, 1 kton liquid scintillator detector in Sud-\nbury mine in Canada, should start its data taking soon, having geoneutrinos among its goals.\nThis is also the case of 50 kton liquid scintillator detector JUNO in Jiangmen, China, which\nwill start its data taking in 2020, towards determination of the neutrino mass hierarchy by\nmeasurement of reactor antineutrinos with 53 km baseline. Inevitable disadvantage of the large\nreactor antineutrino background and a relatively shallow depth, are both balanced by the de-\ntector large size and an excellent energy resolution of 3% at 1MeV. The expected number\nof geoneutrino events is about 400/year [16]. Figure 6 demonstrates a possible measurement\nafter 1 full year of data taking with the expected level of backgrounds [17]. Within the first\nyears, JUNO geoneutrino measurement can quickly exceed the precision of current geoneutrino\nresults.\n\nA real breakthrough in this field would come with the proposed Hanohano [18] project in\nHawaii, a 10 kton movable detector placed underwater. Geological setting on the HPE-depleted\noceanic crust is an ideal location: the mantle contribution to the total geoneutrino flux would\nbe dominant and the principal goal of the geoneutrino measurements, determination of the\nmantle signal, could be reached without the complication of the subtraction of the signal from\nthe local continental crust.\n\nIn conclusion it can be said, that the new field of Neutrino Geoscience has been born and an\ninter-disciplinary community of physicists and geoscientists if developing. The first geoneutrino\n\nMagellan Workshop 2016 7\n\nNEUTRINO GEOSCIENCE\n\nMagellan Workshop 2016 163\n\n\n\nresults confirm the feasibility of these measurements. The measured rates are in agreement\nwith the expectations, confirming that at this level of precision we have a good understanding\nof our Earth. However, there is a potential to learn more about our planet from geoneutrinos:\nand we should take this chance and invest in the next generation of the experiments providing\nnew more precise results.\n\nFor an interested reader, more comprehensive information about geoenutrinos can be found\nin dedicated review articles, as for example [19, 20, 21].\n\nReferences\n[1] O. Šrámek et al., Earth and Plan. Sci. Let. 361 356 (2013).\n\n[2] J.H. Davies and D.R. Davies, Solid Earth 1 5 (2010).\n\n[3] C. Jaupart, S. Labrosse, and J.C. Mareschal, in: D.J. Stevenson (Ed.) Treatise of Geophysics, Elsevier,\nAmsterdam, pp. 1-53, 2007.\n\n[4] Y. Huang et al., Geochem. Geophys. Geosys. 14 2003 (2013).\n\n[5] S. Enomoto, E. Ohtani, K. Inoue, and A. Suzuki, Earth Plan. Sci. Lett 258 147 (2007).\n\n[6] T. Araki et al. (KamLAND Collaboration), Nature 436 499 (2005).\n\n[7] A. Gando et al. (KamLAND Collaboration), Nature Geosc. 4 647 (2011).\n\n[8] A. Gando et al. (KamLAND Collaboration), Phys. Rev. D 88 033001 (2013).\n\n[9] G. Bellini et al. (Borexino Collaboration), Phys. Lett. B 687 299 (2010).\n\n[10] G. Bellini et al. (Borexino Collaboration), Phys. Lett. B 722 295 (2013).\n\n[11] M. Agostini et al. (Borexino Collaboration), Phys. Rev. D 92 031101 (R) (2015).\n\n[12] S. Abe et al. (KamLAND Collaboration), Phys. Rev. Lett. 100 221803 (2008).\n\n[13] G. Bellini et al. (Borexino Collaboration), Phys. Rev. D 89 112007 (2014).\n\n[14] G. Fiorentini et al., Phys. Rev. D 72 033017 (2005).\n\n[15] M. Coltorti et al., Geochim. Cosmochim. Acta 75 2271 (2011).\n\n[16] V. Strati et al., Progress in Earth and Planetary Science 2 5 (2015).\n\n[17] F. An et al. (JUNO Collaboration), J. Phys. G: Nucl. Part. Phys. 43 030401 (2016).\n\n[18] J.G. Learned, S.T. Dye and S. Pakvasa, arXiv: 0810.4975 (2008).\n\n[19] G. Bellini et al., Prog. Part. Nucl. Phys. 73 1 (2013).\n\n[20] L. Ludhova and S. Zavaterlli, Adv. High En. Phys. 2013 425693 (2013).\n\n[21] S.T. Dye, Earth Planet. Sci. Lett. 297 1 (2010).\n\n8 Magellan Workshop 2016\n\nLIVIA LUDHOVA\n\n164 Magellan Workshop 2016\n\n\n\nSN 1987A - Exciting Physics\n\nBruno Leibundgut\n\nESO, Karl-Schwarzschild-Straße 2, 85748 Garching, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/35\n\nObserving the evolution of a supernova over several decades is unique. SN 1987A is the\nonly object where this has been possible in great detail and it has shown us some physics,\nwhich we never had expected. Even now – after 10000 days – does SN 1987A provide new\nsurprises on its way to become a supernova remnant. We present here an overview of the\ncurrent status of the observations of SN 1987A with a focus on the evolution of the emission\nfrom the circumstellar ring. SN 1987A can be detected at all electromagnetic wavelengths,\nwhich gives us a complete picture of the energy sources and some of the radiation transfer.\nThere are several emission sites present in SN 1987A and we will present them in this\noverview.\n\n1 The first two decades of SN 1987A\n\nAs the only celestial source other than the Sun from which so far neutrinos have been observed,\nSN 1987A has been an observational bonanza across the electromagnetic spectrum. It has\nprovided insights in the many different astrophysical topics including stellar evolution, core\ncollapse, nucleosynthesis, explosion physics and the interaction with surrounding material. The\nearly phases of SN 1987A have been summarised in several reviews [1, 2, 3, 4]. The most\nimportant features are the evidence for the core collapse from the neutrino emission, the peculiar\nprogenitor star as a blue supergiant (observed before the explosion), the evidence for mixing\nof elements within the explosion bringing some radioactive material to the surface very early\non and the interaction with circumstellar material, which quickly was identified as being non-\nspherically distributed. In the following, the evidence for radioactive powering of the light\ncurve and energy input from long-lived isotopes like 57Co and 44Ti could be found. Molecule\nformation could be observed directly through the emission of vibrational bands of CO and SiO\nand dust formation in the inner ejecta could be observed through an increased decline of the\nUV-optical-near-infrared (UVOIR) light curve and showed that the inner parts of the explosion\nhad cooled down dramatically. The circumstellar environment of SN 1987A could be imaged and\nthe surrounding torus identified. An outer ring system was also seen in narrow emission lines\nand light echoes from interstellar dust sheets in the Large Magellanic Cloud became obvious\nafter some months. Over the years the supernova shock reaching the equatorial torus could be\nobserved through the increased luminosity of individual knots in the ring.\n\nA particular feature of SN 1987A was its observability from the ground. Its position in the\nLarge Magellanic Cloud is close enough to the celestial south pole that it could be observed\nall year from many southern observatories. Hence the supernova light curve is without major\ngaps. If the SN would have been placed only a few degrees further north, we would have\nlost the maximum phase and also several of the other interesting details of the supernova. Its\n\nMagellan Workshop 2016 1Magellan Workshop 2016 165\n\n\n\nproximity is, of course, the other characteristic which allows us to observe the supernova until\ntoday throughout the electromagnetic spectrum. This is one of the reason that we could learn\nso much from this single object.\n\nThe early light curve (see [4] for a figure which labels the various phases) displays all\nthe characteristics of a core-collapse supernova and some more. The shock breakout can be\ninferred in the UV and the bluest bands as a decrease during the adiabatic expansion phase.\nThe bolometric light curve then rose to a peak within several weeks and ended in a ’plateau’\nphase, which was extremely short due to the compact progenitor star. This is also the reason\nwhy SN 1987A did not reach a peak luminosity comparable to other core-collapse supernovae.\nAfter maximum the light curve settled into a steady decline, which followed the 56Co decay\ntime as all the energy generated in this radioactive decay was thermalised in the ejecta. After\nabout 400 days the light curve started to decline faster because some optical light was blocked\nby dust and re-emitted in the mid-infrared (> 20µm) and was not observed from the ground.\nOnly after about 1000 days did the light curve settle onto a slower decline again as the emission\nwas powered by 57Co with a longer lifetime. After some 2000 days the light curve decline was\nslowed even more due to the thermalisation of γ-rays from 44Ti. After about 2500 days the\nemission from the ring was dominating over the ejecta by about a factor 10 in luminosity. This\nratio has steadily increased over the years due to the supernova ejecta interacting with the\nstationary, dense equatorial torus. The supernova-ring interaction has been studied at many\nwavelengths and has led to a steady increase in flux in all wavebands. It certainly has been the\ndominating feature during the second decade of SN 1987A.\n\nThe second and third decade of SN 1987A are reviewed in [5]. Over the years five emission\nsites from SN 1987A could be distinguished. The inner ejecta (sometimes referred to as ’debris’),\nthe stationary material in the circumstellar torus/ring, the shocked material in the torus, a\nreverse shock between the inner ejecta and the torus, and light echoes from scattered supernova\nlight off interstellar dust sheets between SN 1987A and us. While the inner ejecta are powered\nby the radioactive decays of elements synthesised in the explosion, the ring glowed originally due\nrecombination of stationary material ionised by the supernova shock breakout. The optical ring\nemission started to dominate over the ejecta about 5 years after the explosion as it was fading\nmuch more slowly than the ejecta. After 8 years a first ’hot spot’ on the ring was detected [6, 7]\nand over the following years the ring brightness increased due to the outer supernova ejecta\ncolliding with inward protrusions of ring material, which was observed as a series of bright\nknots (sometimes referred to as a ’pearl necklace’). The full ring evolution over the two decades\nfrom 1994 to 2014 is shown in [8]. The ring brightening could also be observed in the radio [9]\nand at X-ray energies [10] as the shocks from the fast-moving supernova ejecta and the nearly\nstationary ring material built up. The X-ray emission had started to increase already earlier as\nthe shock was making its way through the dense material of an HII region created by the blue\nsupergiant inside the ring [11].\n\n2 The inner ejecta\n\nTheoretical models of core-collapse supernova predict non-symmetric explosions. The reason\nare several hydrodynamic instabilities which are important for the neutrino-driven explosions\n(e.g. [12]). However, it has been so far impossible to observe the geometry of supernova ejecta\ndirectly. SN 1987A provided the possibility to obtain spatially resolved images of the inner\nejecta with the VLT and HST. Integral-field observations in the near-infrared showed that the\n\n2 Magellan Workshop 2016\n\nBRUNO LEIBUNDGUT\n\n166 Magellan Workshop 2016\n\n\n\nextension of the inner ejecta as measured through the 1.64 µm line, which is probably a mixture\nof [Si I] and [Fe II], was at an unexpected angle [13]. If the ring indeed is equatorial to the\nprogenitor star of SN 1987A, then in some models the explosion was expected to be along\nthe stellar poles and hence should be elongated orthogonal to the ring. The VLT SINFONI\nobservations showed that the ejecta lie mostly within the plane of the ring, i.e. inclined nearly 42\ndegrees [13]. Further studies confirmed this geometry, but also indicated a slight misalignment\nof the two lobes relative to the centre [14]. The appearance in the Hα emission line at 10000\ndays after explosion is markedly different from the one in [Si I]. The Hα image shows a shell-like\nstructure with an emission hole near the centre, while the [Si I] image displays a filled, centrally\nlocated emission.\n\nThe inner ejecta had been fading for many years as the energy input from the radioactive\ndecays was dwindling. However, [15] found that around 5500 days after explosion the ejecta\nstarted to increase in brightness at optical wavelengths again. This is a rather unexpected\ndevelopment as an extra energy source is needed. Radioactivity only provides diminishing\nenergy with time and radiation effects, e.g. changes in the opacity, are not expected at such\na late phase. It had to be external energy input and the X-ray radiation from the supernova\nshock with the circumstellar ring was identified as the culprit. The X-rays penetrate back into\nthe inner ejecta and ionise the hydrogen creating the shell-like appearance. The silicon on the\nother hand is still powered by the radioactive decay of 44Ti [15, 8].\n\nMid- and far-infrared measurements of SN 1987A were only enabled by the Spitzer and\nHerschel satellites around 9000 days after explosion. These are typically combined with mil-\nlimetre wavelength observations from the ground. A large dust mass (up to half a solar mass)\nwas inferred from these late observations [16], which needs to be compared to the dust mea-\nsurement at 1150 days after the explosion (3 · 10−3M⊙; [17]). Over the 22 years between these\nobservations the refractory material must have formed the dust we observe today. Since ALMA\nbecame operational, it observed he dust continuum emission [18] and vibrational transitions of\ncarbon monoxide (CO) and silicon oxide (SiO) [19]. The inner ejecta must be filled with cold\n(<50 K) dust. Another molecule found in the inner ejecta is H2, which was observed after 6500\ndays [20]. It was predicted to be formed with CO and SiO, but was only recently detected in\nthe near infrared. It is concentrated towards the core of the ejecta similar to the [Si I]/[Fe II]\nemission and most probably is heated by the radioactive decays.\n\nThe evolution of the inner ejecta is unclear. The X-rays will penetrate further into the\nejecta and the radioactive input will decrease continuously, although with a long (44Ti has a 60\nyear half-life) decay time. It will also expand in size and eventually will reach the circumstellar\nring.\n\n3 The equatorial ring\n\nSN 1987A is surrounded by a circumstellar ring, whose origin remains unclear. It could be the\nremnant of a stellar merger shortly before explosion, but the details of such a scenario are not\nresolved (e.g. [5]). It is clear from the mottled appearance with many individual hot spots that\nthe ring is not uniform. It rather seems that it is broken up into clumps and inward protrusions\ndue to some dynamic instability during the formation process. We can distinguish shocked and\nunshocked gas in the ring. The emission from unshocked is coming from essentially stationary\nmaterial moving with ∼10 km s−1 and ionised through the soft X-rays from the shock. The\nshocked gas has been ionised and accelerated to about >300 km s−1 by the supernova forward\n\nMagellan Workshop 2016 3\n\nSN 1987A - EXCITING PHYSICS\n\nMagellan Workshop 2016 167\n\n\n\nshock [21, 22, 23]. Some of the ionisation is so high that coronal lines, e.g. of [Fe XIV] are\nobserved. High-resolution spectroscopy, even if the ring cannot be not spatially resolved, hence\nallows us to separate the different emission sites based on the observed velocities. In the near-\ninfrared integral-field spectroscopy has yielded both spatial and spectral information. In this\ncase the orientation of the ring could be determined by tracing the expanding ring on the sky\n[24]. This confirmed the previous geometry derived from HST imaging [25, 26]. The ring is\ninclined by 43 degrees with the northern part closer to us and a rotation out of the sky of\n9 degrees with the eastern part slightly closer than the western ring. The systemic velocity\nof the ring is 286.5 km s−1, which can be compared to the systemic velocity of the Large\nMagellanic Cloud of 278 km s−1 [27].\n\nThe first ring region reached by the supernova shock was to the north-east at around 4000\ndays after explosion. The ring then brightened through the north and the east wrapping around\nfinally to the western part of the ring. This process took about 5 years [8]. The ring emission\nwas maximum shortly after 8000 days (in B and R filters) and started to decline thereafter.\nThis evolution is nicely traced by emission lines from the shocked gas, mostly Hα, [O III] 5007Å,\nand coronal lines like [Fe XIV] 5303Å. The narrower lines from the unshocked gas, Hα, [O III]\nand [N II] 6583Å, declined already after 7000 days. The peak is reached later with increasing\nwavelength when observing into the infrared [28]. The fading of the ring is an indication that\nthe shock has now overrun most of the ring material and is moving beyond the ring. Based on\nthe fading of the individual emission lines the ring will become unobservable by around 2020\n[8].\n\nThe optical peak brightness was reached at different times with the NE peaking around 6800\ndays and the SW only after 8500 days following roughly the time lag of the first appearance of\nthe spots. The brightest parts now are in the west and the spots in this region have on average\nalso reached a higher flux peak. Until today the ring emission is not been fully closed [14].\n\nThe ring is also observed in synchrotron emission at radio wavelengths. Here the shocks\naccelerate the electrons in the magnetic field. The shocks can be directly seen in X-rays, where\nthe ring is resolved in Chandra observations [10]. Contrary to the optical the X-rays have not\npeaked yet, which is an indication that the shock is still running through lower density material,\nwhich does not radiate in the optical.\n\nRecent HST observations have revealed new emission sites outside the equatorial ring [8].\nThe supernova forward shock has overrun the ring in several places and starts to interact with\nclouds outside the ring. Projection effects also have to be taken into account as the ring is\ninclined and we may see the shock outside the ring plane starting to interact with material\nabove (or below) the ring plane. Since the shock has been decelerated in the ring, parts in\nother directions have now reached further out and eventually will start to interact with any\nmaterial in its way. In particular, we should be able to distinguish whether there is a continuous\nconnection between the inner and the outer rings and the shock should encounter such material.\nIt will reveal the gas distribution in the wider surroundings of the supernovae.\n\n4 The reverse shock\n\nA reverse shock has built up between the forward shock moving through the equatorial ring and\nthe inner ejecta. This is the outer ejecta catching up with the slowed down forward material\nencountering the dense equatorial ring and other material in the predicted HII region [11]. The\nreverse shock is characterised through high velocity (>10000 km s−1) hydrogen lines (Lyα, Hα,\n\n4 Magellan Workshop 2016\n\nBRUNO LEIBUNDGUT\n\n168 Magellan Workshop 2016\n\n\n\nHβ), which emerge from the ionised ejecta flowing into the shock region, where material has\nbeen decelerated behind the forward shock. SN 1987A has developed a strong reverse shock\naround 1997 [6, 29] and its flux has increased continuously until 2009 (more than 9000 days\nafter explosion; [31, 32, 33]). A detailed description of the reverse shock in SN 1987A has\nbeen provided in [30] for the location and by [33] describing the temporal evolution of the\nspectral appearance. The direct imaging has provided evidence that the reverse shock emission\nhas been observed outside the equatorial ring plane towards the outer rings [30], which is an\nindication that the SN shock has expanded into the hourglass shaped region connecting the\nouter rings with the equatorial ring. This is further supported by the large observed velocity,\nwhich indicates that there has to be flux outside the equatorial ring [8]. The outer ejecta appear\nto be heated by X-rays coming from the shocks and display a very high ratio of Lyman-α to\nBalmer-α emission, which leads to an increased Lyman-α flux. The flux evolution follows nearly\nexactly the soft X-ray increase, which is a clear indication that the heating of the reverse shock\nmaterial is done through X-rays. An early prediction that the Hα emission from the reverse\nshock will disappear by around 2014 due to the full ionisation of hydrogen before it reaches the\nshock region [31] has not been confirmed, but there appears to be a flattening of the flux coming\nfrom the reverse shock. This could mean that the reverse shock in the equatorial plane has\nreached the intrusions from the inner ring [33]. The emission outside the equatorial ring will\nincrease in the coming years outlining the material distribution outside the equatorial plane\nand mapping the mass loss history of the progenitor star of SN 1987A. Future observations\nwill map the evolution of the reverse shock in SN 1987A and hence the circumstellar matter\ndistribution around the supernova explosion.\n\n5 The compact object\n\nThe neutrino signal from SN 1987A indicated the core collapse to a neutron star. However,\nno signature of the neutron star or a possible black hole, if the neutron star due to fallback of\nmaterial not expelled in the explosion became unstable and collapsed further, has so far been\ndetected. A young neutron star is expected to rapidly lose energy as it spins down within a\nmagnetic field. A resulting pulsar wind nebula, similar to the one observed in the Crab nebula\nshould be expected, although the observed upper limits on the luminosity of a compact object\nin SN 1987A is already below the one observed in the Crab.\n\nThe distance of SN 1987A makes it difficult to isolate a central compact object in X-rays and\nat radio wavelengths from the ejecta, reverse shock and the ring interaction. The X-rays would\nbe the most promising wavelength region for the detection of the plasma ionised by the pulsar\nwind. So far, no detection has been reported. Also, the synchrotron radiation could be detected\nat radio frequencies. No central point source has been seen to date (e.g. [34, 35]). Emission at\noptical wavelengths is difficult to detect as the dust, which formed in the inner ejecta (e.g. [18])\ncould block the line of site towards the centre. An attempt to detect UV continuum emission\nbetween the many emission lines has been done some 10 years ago [36] setting the currently\nstrongest limits of less than 1.3 L⊙. The lack of a detection of the compact source means that\nthe magnetic field of the neutron star is fairly weak as a Crab-like plerion should have been\ndetected already.\n\nMagellan Workshop 2016 5\n\nSN 1987A - EXCITING PHYSICS\n\nMagellan Workshop 2016 169\n\n\n\n6 The future\n\nSN 1987A continues its evolution towards a supernova remnant. The recent years have shown\nsurprises, like the formation of massive dust in the ejecta, the additional heating of the hydrogen\nlayers in the inner ejecta from the X-rays created in the shocks and the lack of emission from\nthe central compact object, which must have formed in the collapse. Several predictions on\nhow SN 1987A would evolve have been confirmed. Among them are the continued interaction\nwith the equatorial ring, the emission coming from outside the ring and the strengthening of\nthe reverse shock over the years. These also set the expectations on what the future will bring.\nForemost the ring will be overrun by the shock in a few years and its emission will start to drop.\nThe structure of the inner ejecta will be revealed further through the emission evolution due to\nthe different heating mechanisms. The reverse shock will start to weaken when it approaches\nthe ring in the equatorial plane, but will continue to map out the circumstellar structure outside\nthat plane. It will be interesting to see whether more dust is forming in the inner ejecta and\nwhen the cold dust shell between the forward and reverse shocks will become detectable. The\nsearch for the central compact object will continue and it will hopefully become observable in\nthe coming years.\n\nThere is still a lot in store for observers of SN 1987A. Eventually, this supernova will be\njoined by other objects detected through their neutrino emission. The prospects are excellent\nthat we may be witnessing such an event in the not too distant future.\n\n7 Acknowledgments\n\nThis research was supported by the DFG Cluster of Excellence ’Origin and Structure of the\nUniverse’ and a DFG Transregio network TRR33 ’The Dark Universe’.\n\nReferences\n[1] Arnett, W. D., Bahcall, J. N., Kirshner, R. P., & Woosley, S. E. 1989, ARA&A, 27, 629\n\n[2] McCray, R. 1993, ARA&A, 31, 175\n\n[3] McCray, R. 2003, Supernovae and Gamma-Ray Bursters, 598, 219\n\n[4] Fransson, C., Gilmozzi, R., Groeningsson, P., et al. 2007, The Messenger, 127, 44\n\n[5] McCray, R., Fransson, C. 2016, ARA&A, 54, 19\n\n[6] Sonneborn, G., Pun, C. S. J., Kimble, R. A., et al. 1998, ApJL, 492, 139\n\n[7] Lawrence, S. S., Sugerman, B. 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A., et al. 2005, ApJ, 629, 944\n\nMagellan Workshop 2016 7\n\nSN 1987A - EXCITING PHYSICS\n\nMagellan Workshop 2016 171\n\n\n\n\n\nConnecting Neutrino Physics\nand Astronomy\n\nPoster Contributions\n\nMagellan Workshop 2016 173\n\n\n\n\n\nThe SOX experiment: understanding the detec-\n\ntor behavior using calibration sources\n\nA. Caminata1, M. Agostini2, K. Altenmüller2, S. Appel2, G. Bellini3,J. Benziger4, N. Berton5,\nD. Bick6, G. Bonfini7, D. Bravo8,B. Caccianiga3, F. Calaprice9, M. Carlini7, P. Cavalcante7,\nA. Chepurnov10, K. Choi11, M. Cribier5, D. D’Angelo3, S. Davini12, A. Derbin13, L. Di\nNoto1, I. Drachnev12, M. Durero5, A. Etenko14, S. Farinon1, V. Fischer5, K. Fomenko15,\nA. Formozov3,15, D. Franco16, F. Gabriele7, J. Gaffiot5, C. Galbiati9, C. Ghiano1, M. Giammarchi3,\nM. Goeger-Neff2, A. Goretti9, M. Gromov10, C. Hagner6, Th. Houdy5, E. Hungerford17,\nAldo Ianni7, Andrea Ianni9, N. Jonquères18, K. Jedrzejczak19, D. Jeschke2, M. Kaiser6,\nV. Kobychev20, D. Korablev15, G. Korga7, V. Kornoukhov21, D. Kryn16, T. Lachenmaier22,\nT. Lasserre5, M. Laubenstein7, B. Lehnert23, J. Link8, E. Litvinovich14,24, F. Lombardi7,\nP. Lombardi3, L. Ludhova3, G. Lukyanchenko14,24, I. Machulin14,24, S. Manecki8,W. Maneschg28,\nS. Marcocci12, J. Maricic11, G. Mention5, E. Meroni3, M. Meyer6, L. Miramonti3, M. Misiaszek7,19,\nM. Montuschi25, P. Mosteiro9, V. Muratova13, R. Musenich1, B. Neumair2, L. Oberauer2,\nM. Obolensky16, F. Ortica26, M. Pallavicini1, L. Papp2, L. Perasso1, A. Pocar27, G. Ranucci3,\nA. Razeto7, A. Re3, A. Romani26, R. Roncin7,16, N. Rossi7, S. Schönert2, L. Scola5, D. Semenov13,\nH. Simgen28, M. Skorokhvatov14,24, O. Smirnov15, A. Sotnikov15, S. Sukhotin13, Y. Suvorov14,29\n\nR. Tartaglia7, G. Testera1, J. Thurn23, M. Toropova14, C. Veyssiére5, M. Vivier5, E. Unzhakov13,\nR.B. Vogelaar8, F. von Feilitzsch2, H. Wang29, S. Weinz30, J. Winter30, M. Wojcik19, M. Wurm30,\nZ. Yokley8, O. Zaimidoroga15, S. Zavatarelli1, K. Zuber23, G. Zuzel19\n\n1Dipartimento di Fisica, Universitá degli Studi e INFN, Genova 16146, Italy\n2Physik-Department and Excellence Cluster Universe, Technische Universität München, 85748\nGarching, Germany\n3Dipartimento di Fisica, Universitá degli Studi e INFN, 20133 Milano, Italy\n4Chemical Engineering Department, Princeton University, Princeton, NJ 08544, USA\n5Commisariat à l’Énergie Atomique et aux Énergies Alternatives, Centre de Saclay, IRFU,\n91191 Gif-sur-Yvette, France\n6Institut für Experimentalphysik, Universität Hamburg, 22761 Hamburg, Germany\n7INFN Laboratori Nazionali del Gran Sasso, 67010 Assergi (AQ), Italy\n8Physics Department, Virginia Polytechnic Institute and State University, Blacksburg, VA\n24061, USA\n9Physics Department, Princeton University, Princeton, NJ 08544, USA\n10Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics, 119234 Moscow,\nRussia\n11Department of Physics and Astronomy, University of Hawai’i, Honolulu, HI 96822, USA\n12Gran Sasso Science Institute (INFN), 67100  L’Aquila, Italy\n13St. Petersburg Nuclear Physics Institute NRC Kurchatov Institute, 188350 Gatchina, Russia\n14NRC Kurchatov Institute, 123182 Moscow, Russia\n15Joint Institute for Nuclear Research, 141980 Dubna, Russia\n\nMagellan Workshop 2016 1Magellan Workshop 2016 175\n\n\n\n16AstroParticule et Cosmologie, Universit Paris Diderot, CNRS/IN2P3, CEA/IRFU, Observa-\ntoire de Paris, Sorbonne Paris Cit, 75205 Paris Cedex 13, France\n17Department of Physics, University of Houston, Houston, TX 77204, USA\n18Commisariat à l’Énergie Atomique et aux Énergies Alternatives, Centre de Saclay,\nDEN/DM2S/SEMT/BCCR, 91191 Gif-sur-Yvette, France\n19M. Smoluchowski Institute of Physics, Jagiellonian University, 30059 Krakow, Poland\n20Kiev Institute for Nuclear Research, 06380 Kiev, Ukraine\n21Institute for Theoretical and Experimental Physics, 117218 Moscow, Russia\n22Kepler Center for Astro and Particle Physics, Universität Tübingen, 72076 Tübingen, Ger-\nmany\n23Department of Physics, Technische Universität Dresden, 01062 Dresden, Germany\n24National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409\nMoscow, Russia\n25Dipartimento di Fisica e Scienze della Terra Università degli Studi di Ferrara e INFN, Via\nSaragat 1-44122, Ferrara, Italy\n26Dipartimento di Chimica, Università e INFN, 06123 Perugia, Italy\n27Amherst Center for Fundamental Interactions and Physics Department, University of Mas-\nsachusetts, Amherst, MA 01003, USA\n28Max-Planck-Institut für Kernphysik, 69117 Heidelberg, Germany\n29Physics and Astronomy Department, University of California Los Angeles (UCLA), Los An-\ngeles, California 90095, USA\n30Institute of Physics and Excellence Cluster PRISMA, Johannes Gutenberg-Universität Mainz,\n55099 Mainz, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/22\n\nThe SOX experiment investigates the existence of light sterile neutrinos. A solid signal\nwould mean the discovery of the first particles beyond the Standard Electroweak Model and\nwould have profound implications in our understanding of the Universe and of fundamental\nparticle physics. In case of a negative result, it is able to close a long standing debate\nabout the reality of the neutrino anomalies. The SOX experiment will use a 144Ce−144Pr\nantineutrino generator placed 8.5 m below the Borexino liquid scintillator detector. In\nview of the SOX experiment, a precise knowledge of the energy response and the spatial\nreconstruction of the antineutrino events is very important. Consequently, a calibration\ncampaign of the Borexino detector is foreseen before the beginning of the SOX data taking.\nThis paper briefly reviews the techniques used for calibrate the Borexino detector.\n\n1 Introduction\n\nAlthough most of the collected neutrino experimental data are explainable within the three-\nflavor oscillation model, some short-baseline neutrino experiment have reported results that\nsignificantly deviates from the three active neutrino picture ([1, 2, 3, 4]), suggesting a possible\nshort distance oscillation between the three known neutrino states and one hypothetical new\nstate with a squared mass ∆m2\n\n14 of about 1 eV 2. Due to constraints on Z boson coupling\nasserted by LEP, this state would be sterile.\n\nThe SOX project aims to test the existence of light sterile neutrinos detecting the antineu-\n\n2 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n176 Magellan Workshop 2016\n\n\n\ntrino flux coming from a 150 kCi 144Ce −144 Pr source placed at short distance from the\nBorexino liquid scintillator detector [5] (figure 1). Antineutrinos are detected inside Borexino\nvia inverse beta decay, thus with a negligible background [6]. Borexino will investigate short\ndistance neutrino oscillations in two ways. The first way is the standard disappearance tech-\nnique (rate technique). Knowing the source activity and measuring the interaction rate, it is\npossible to investigate the existence of the sterile neutrino state. The second way is based on an\noscillometry measurement within the detector volume (shape technique). Expected sensitivities\nare shown in figure 2.\n\nFigure 1: Schematic drawing of the Borexino\ndetector.\n\nFigure 2: Neutrino anomaly region and fore-\nseen sensitivity for SOX.\n\n2 Motivation for a calibration campaign and Borexino\nsource insertion system\n\nA good knowledge of the Borexino detector response is a key requirement for the SOX analysis.\nGiven the large dimensions of the detector, it is necessary to map the energy response and the\nposition reconstruction accuracy in the whole active volume. Since the energy of the incoming\nantineutrino is reconstructed from the positron energy, the knowledge of the energy scale is\nmandatory. Consequently, a Monte Carlo simulation code have been developed and the detector\nresponse have been tested deploying radioactive sources in several positions (2008 and 2009\ncalibration campaigns [7]). These data are extremely useful in view of the SOX experiment.\nSince the SOX and geoneutrino signals have the same features, calibration data acquired for\nthe geoneutrino analysis can be used also for SOX. Nevertheless, a new calibration campaign\nis foreseen before the arrival of the SOX antineutrino source. These calibration data are of\nextreme importance in understanding the detector behavior and to increase the reliability of\nthe SOX Monte Carlo simulation code. Since in the SOX analysis the cut efficiencies and the\nsensitivity studies are performed analyzing the output of the simulations, having an accurate\nsimulation code is crucial for a proper data analysis.\n\nSources are inserted in Borexino by means of a series of hollow interconnecting rods in a 4′′\n\npipe connecting the top of the detector with the liquid scintillator (figure 1). One special rod has\n\nMagellan Workshop 2016 3\n\nTHE SOX EXPERIMENT: UNDERSTANDING THE DETECTOR BEHAVIOR USING . . .\n\nMagellan Workshop 2016 177\n\n\n\nFigure 3: Left: picture shot by a CCD camera. The insertion system is visible and it is pointed\nby the blue arrow. Right: vials used for gamma (top) and neutron (bottom) sources.\n\nan hinge that allows the arm to bend up to 90◦ and thus reaching most of the detector’s active\nzone. Since it is important to know the position of the inserted source, a source location system\nhave been developed. The system has to find the exact coordinate of the position in which\nthe source is deployed. This information is crucial for estimating the position reconstruction\naccuracy. Consequently, seven CCD cameras look for the position of a red (wavelength about\n630 nm) light diffuser attached close to the source (figure 3). The system is able to locate the\nsource within 1 cm. The wavelength of this light has been chosen so as not to be harmful to\nthe PMTs under HV.\n\n3 Calibration campaign\n\n3.1 Past calibration campaign\n\nGamma radioactive sources were dissolved in aqueous solution and contained in small quartz\nvials. α and β radioactive sources (14C −222 Rn) were contained in a quartz vial as well but\nscintillator was used as solvent. Table 1 shows all the sources used for calibration purpose.\n\nA deep understanding of the detector response to the gamma sources is fundamental for the\nSOX analysis since the energy deposit of the prompt event is due to both positron interaction\nwith matter and to positron-electron annihilation.\n\nThe 241Am−9 Be neutron source calibration was performed mainly for solar neutrino (8B)\nand geoneutrino analyses. Nevertheless, they are of fundamental importance for SOX, since\nthe delayed event is due to neutron capture. In the 241Am −9 Be neutron source, neutrons\nare produced in association with gammas, with a total energy releas of 4.44 MeV. Similarly\nto the SOX signal, the 241Am−9 Be source provides a prompt and a delayed event (figure 4).\nThe prompt event is due to the gamma energy loss together with the signal of proton recoils\n\n4 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n178 Magellan Workshop 2016\n\n\n\nSource Type E [MeV] Position Motivations Campaign\n57Co γ 0.122 in IV volume Energy scale IV\n139Ce γ 0.165 in IV volume Energy scale IV\n203Hg γ 0.279 in IV volume Energy scale III\n85Sr γ 0.514 z-axis + sphere R=3m Energy scale + FV III,IV\n54Mn γ 0.834 along z-axis Energy scale III\n65Zn γ 1.115 along z-axis Energy scale III\n60Co γ 1.173, 1.332 along z-axis Energy scale III\n40K γ 1.460 along z-axis Energy scale III\n\n222Rn+14C β,γ 0-3.20 in IV volume FV+uniformity I-IV\nα 5.5, 6.0, 7.4 in IV volume FV+uniformity\n\n241Am9Be n 0-9 sphere R=4m Energy scale + FV II-IV\n394 nm laser light - center PMT equalization IV\n\nTable 1: Radioactive sources used during the Borexino internal calibration campaigns. The\nradionuclides, energies and emitted particle types are shown in the first three columns. The\nfourth column indicates the positions where the sources were deployed within the scintillator.\nThe main purposes for the individual source measurements are summarized in the fifth column.\nThe last column indicates in which campaign the sources have been deployed: I (October 2008),\nII (January 2009), III (June 2009) and IV (Jul 2009). FV is the fiducial volume used in the solar\nanalysis [8], the corresponding sources were used to evaluate the event position reconstruction\nperformances.\n\ndue to interaction of neutron with the scintillator. Afterwards, neutrons thermalize in the\nhydrogen-rich organic scintillator and are captured either on protons or carbon nuclei, emitting\ncharacteristic 2.22 MeV and 4.95 MeV γ rays (figure 4). These γ rays produce a delayed signal\naccording to the neutron capture time of about 254 µs.\n\n3.2 Next calibration campaign\n\nNext calibration campaign will take place next fall just before the SOX source arrival. During\nnext calibration campaign both gamma and AmBe sources will be deployed inside Borexino.\nThis calibration will be very useful both for phase 2 solar neutrino analysis (precision measure-\nment of solar neutrino fluxes) and for SOX. Differently from old calibration campaigns, there\nwill be an extensive calibration with AmBe source, especially nearby the inner vessel. The\ncalibration points at large radii will be extremely important to study the neutron detection\nefficiency at the border of the active zones.\n\n4 Conclusions\n\nThe SOX experiment aims to investigate the existence of sterile neutrinos placing a high activity\n144Ce−144Pr antineutrino source below the Borexino detector. Both a rate analysis and a shape\nanalysis are foreseen. For both the analyses, a deep understanding of the detector response is\nnecessary. In the past years, the detector have been calibrated deploying several sources in the\nactive volume. A new calibration campaign is foreseen for the end of this year, just before the\nbeginning of SOX data-tacking.\n\nMagellan Workshop 2016 5\n\nTHE SOX EXPERIMENT: UNDERSTANDING THE DETECTOR BEHAVIOR USING . . .\n\nMagellan Workshop 2016 179\n\n\n\nFigure 4: Energy spectrum of the 241Am−9Be source. The spectrum is subdivided in neutron-\ninduced prompt signal (blue) and delayed signals (red).\n\nReferences\n[1] Aguilar A et al. 2001 Phys. Rev. D 64 112007\n\n[2] Aguilar A et al. 2013 Phys. Rev. Lett. 110 161801\n\n[3] Mention G et al. 2011 Phys. Rev. D 83 073006\n\n[4] Giunti C and Laveder M 2011 Phys. Rev. C 83 065504\n\n[5] Bellini G et al. 2013 JHEP 38 1308\n\n[6] Agostini M et al. 2015 Phys. Rev. D 92 031101\n\n[7] Bellini G et al. 2012 Journal of Instrumentation 7 10018\n\n[8] Bellini G et al. 2014 Phys. Rev. D 89 112007\n\n6 Magellan Workshop 2016\n\nALESSIO CAMINATA et al.\n\n180 Magellan Workshop 2016\n\n\n\nNeutron Shielding Simulations and Muon-induced\n\nNeutrons\n\nLaura Vanhoefer1, Iris Abt1, Lucia Garbini1, Raphael Kneißl1, Heng-Ye Liao1, Béla Majorovits1,\nMatteo Palermo1, Oliver Schulz1\n\n1Max-Planck-Institut für Physik, Föhringer Ring 6, 80805 München, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/41\n\nCosmic-ray neutrons and muons can produce background in low-rate experiments due to\nthe production of long-lived radioisotopes during transport above ground. This kind of\nbackground can be reduced by shielding the detector components during transport.\nCosmic-ray neutron and muon (µ+ and µ−) shielding simulations with the GEANT4 based\nframework MaGe were done to estimate the shielding properties of water, plastic, soil, steel,\nconcrete, copper, LNGS rock and lead. The shielding power of cosmic-ray neutrons and\nmuon-induced neutrons as well as the total neutron fluxes at different shielding depths are\npresented. Also the neutron backscattering effect was investigated and was found to be\nsignificant.\n\n1 Introduction\n\nCosmic-ray neutrons and muons which are produced in the Earth’s atmosphere can produce\nbackground in low-rate experiments like 0νββ experiments. Any energy deposition near or\nabove the Q-value of the 0νββ decay is background for such experiments. Radioisotopes which\nproduce such energy depositions can be created by neutrons and muons via spallation, neutron\ncapture, (n,p) reaction etc. Also inelastic scattering where the nucleus is put into an excited\nstate can produce an energy deposition near or above the Q-value. Long-lived radioisotopes\nwith half-lives of many years are still present during measurements in an underground labora-\ntory. This kind of background can be reduced by restricting the overall exposure to cosmic-ray\nradiation and by shielding of the detector components during transport above ground.\nTo compare different possible shielding materials, cosmic-ray neutron and muon shielding sim-\nulations were done using the GEANT4 based framework MaGe [1] from the GERDA and\nMajorana collaboration to estimate the shielding properties of plastic, water, soil, concrete,\nLNGS rock [2], steel, copper and lead (see also [3]).\nIn the simulations, blocks from different shielding materials were exposed to neutrons, µ+ and\nµ−. The number of neutrons and muons which leave the block at the other side are counted.\nSince the particles scatter inside the material, particles can scatter back whichs leads to multiple\ncounting of particles. The investigation of the backscattering effect of neutrons is described in\nsection 2. In section 3, the results of the neutron simulations are discussed. The results of the\nmuon simulations are presented in section 4. Using the cosmic-ray neutron and muon-induced\nneutron fluxes, the total neutron flux can be calculated for each material. These results are\nshown in section 5.\n\nMagellan Workshop 2016 1Magellan Workshop 2016 181\n\n\n\nFigure 1: Illustration of the backscattering\neffect.\n\nshielding depth x [m]\n0 2 4 6 8 10 12 14\n\nn,\nxm\n\n b\nlo\n\nck\n / \n\nN\nn,\n\n20\nm\n\n b\nlo\n\nck\nN\n\n0\n\n1\n\n2\n\n3\n\n4\n\n5\n\nPE shield\n\nWater shield\n\nSteel shield\n\nSoil shield\n\n only downwards going neutrons\n\nFigure 2: Ratio of neutrons counted inside a\nblock and neutrons leaving blocks with differ-\nent thicknesses.\n\nMaterial Plastic Water Soil Steel\n\nAll neutrons 1.3552 ± 0.0006 1.4918 ± 0.0005 2.1718 ± 0.0005 5.340 ± 0.002\nDownwards going neutrons 1.1370 ± 0.0005 1.1899 ± 0.0004 1.4723 ± 0.0004 2.9322 ± 0.0009\nUpwards going neutrons 0.218 ± 0.001 0.3019 ± 0.0008 0.700 ± 0.001 2.408 ± 0.008\n\nTable 1: Neutron flux inside a 20m block at a certain shielding depth divided by the neutron\nflux behind a block with the corresponding thickness.\n\n2 Backscattering effect\n\nDuring particle transport in the simulation, the particle properties like momentum, energy\nand position are only stored if a boundary is crossed. Usually it is not monitored how often\na particle crosses the same boundary. Therefore backscattering can lead to double or even\nmultiple counting of neutrons. This can lead to wrong shielding indices. To estimate the\nfraction of backscattered neutrons, a 20m block, which is divided into 20 sub-blocks, was\nsimulated. The total neutron flux and the neutron flux pointing downwards are derived at\ndifferent shielding depths inside the block. These numbers are then compared to the number\nof simulated neutrons leaving a block with the corresponding thickness. This is illustrated by\nFig. 1. The ratio between the number of neutrons between the 20m block and the block with\nthe corresponding thickness is a measure of backscattering and is shown in Fig. 2. Without\nbackscattering, the ratio would be one. The larger the ratio, the more neutrons scatter back.\nFrom Fig. 2 it can be seen that backscattering does not depend on the depth. Therefore the\nratio can be obtained by averaging over all depths. The results are shown in Table 1. It\ncan clearly be seen that backscattering is larger for denser materials with higher average mass\nnumber A like steel and that the backscattering effect is significant. Therefore one block for\neach shielding thickness has to be simulated. Further, the material blocks need to be placed in\nvacuum to avoid miscounting at the boundary of the block.\n\n2 Magellan Workshop 2016\n\nLAURA VANHOEFER et al.\n\n182 Magellan Workshop 2016\n\n\n\nEnergy [MeV]\n1 10 210 310 410 510\n\nM\neV\n\ns]\n2\n\nF\nlu\n\nx \n[1\n\n/c\nm\n\n-1310\n\n-1210\n\n-1110\n\n-1010\n\n-910\n\n-810\n\n-710\n\n-610\n\n-510\n\n-410\n\n-310\n\n-210\n\nCosmic-Ray Neutron Energy Spectrum (Gordon)\n\nFigure 3: Used neutron energy spectrum. Datapoints are taken from [4].\n\n3 Neutron simulations\n\nFor each shielding depth and material one block was simulated. Neutrons are injected follow-\ning the energy spectrum shown in Fig. 3 measured by Gordon et al. [4]. Since the angular\ndistribution of cosmic-ray neutrons is not well known, also the effect of using different an-\ngular distributions was investigated. For this purpose, a distribution where all neutrons are\ngoing only vertically downwards and an isotropic distribution were chosen, since the on aver-\nage penetrated distance is the smallest for the first distribution and the largest for the second\ndistribution. Also a distribution suggested by A. Nesterenok [5] for high-energetic cosmic-ray\nneutrons (E ≥ 10MeV) reaching the ground\n\nJ(θ) = J0 exp[α(1 − cos θ)] with α = −2.5 (1)\n\nwas investigated. In Eq. 1, J0 is the flux in the vertical direction and θ is the nadir angle.\nFigure 4 shows the energy spectra of cosmic-ray neutrons after penetrating steel blocks with\ndifferent thicknesses. Only vertically downwards going neutrons were injected. A shielding\nthickness of 0 dm corresponds to the injected spectrum which is the Gordon spectrum shown\nin Fig 3. During penetration, neutrons lose energy by scattering off shielding material nuclei.\nSome neutrons are stopped, but also new neutrons and other particles are created. After a\ncertain depth, the shower reaches equilibrium. When equilibrium is reached, the shape of the\nenergy spectrum is not changing anymore and more particles are shielded than produced. In\nFig. 4, it can be seen that the equilibrium for steel is reached after 6 dm of shielding. The\ndepths at which equilibrium is reached is also listed in Table 2 for all simulated materials.\nAfter the equilibrium is reached, the decreasing neutron flux with increasing shielding depth\ncan be described by a power law fit [6]:\n\ny = 10sx+a (2)\n\nwith the shielding depth x, the neutron flux y(x), the shielding index s and a fit constant a.\nFigure 5 shows the number of neutrons normalized to the number of arriving neutrons for\ndifferent shielding depths and materials. The results of the power law fits are shown in Table 2.\n\nMagellan Workshop 2016 3\n\nNEUTRON SHIELDING SIMULATIONS AND MUON-INDUCED NEUTRONS\n\nMagellan Workshop 2016 183\n\n\n\nhist_WithoutAngDist_Steel_1dm\n\nEntries  5810486\nMean   0.1776\nRMS     3.749\n\nEnergy [MeV]\n-610 -510 -410 -310 -210 -110 1 10 210 310 410 510\n\nD\niff\n\ner\nen\n\ntia\nl F\n\nlu\nx \n\n[1\n/M\n\neV\n]\n\n-810\n\n-710\n\n-610\n\n-510\n\n-410\n\n-310\n\n-210\n\n-110\n\n1\n\n10\n\n210\n\n310\n\n410\nhist_WithoutAngDist_Steel_1dm\n\nEntries  5810486\nMean   0.1776\nRMS     3.749\n\n0 dm\n\n1 dm\n\n3 dm\n\n6 dm\n\n9 dm\n\n12 dm\n\n15 dm\n\nSteel: Neutrons\n\nFigure 4: Simulated neutron energy spectrum at different depths for steel. Only downwards\ngoing neutrons were injected.\n\nMaterial EQ depth Only downwards going neutrons Nesterenok Isotropic\n\nPlastic < 0.1 −0.354± 0.003 −0.366± 0.002 −0.388 ± 0.005\nWater < 0.1 −0.372± 0.003 −0.397± 0.007 −0.418 ± 0.005\nSoil < 0.1 −0.57± 0.01 −0.584± 0.007 −0.611 ± 0.009\n\nConcrete 0.1± 0.05 −0.822± 0.006 −0.855± 0.004 −0.91 ± 0.02\nLNGS rock 0.6± 0.05 −0.850± 0.003 −0.897± 0.005 −0.961 ± 0.009\n\nSteel 0.6± 0.05 −2.01± 0.02 −2.12± 0.02 −2.22 ± 0.03\nCopper 0.7± 0.05 −2.10± 0.02 −2.15± 0.01 −2.22 ± 0.02\nLead 1.6± 0.05 −1.368± 0.009 −1.39± 0.01 −1.42 ± 0.01\n\nTable 2: Shielding index s [1/m] for different materials and angular distributions (E≥ 0MeV).\nThe depth at which equilibrium of the shower is reached (EQ depth [m]) is also shown.\n\nMore neutrons are shielded if the shielding index is smaller. This is the case for denser materials\nwith higher mass number A. Comparing the shielding indices of different angular distributions\nfor the same material, it can be seen that the shielding indices of the isotropic distribution are\nalways the smallest and the shielding indices of the only vertically downwards going neutron\ndistribution is always larger. This can be explained by the fact that the on average penetrated\ndistance is larger for the isotropic distribution. It can also be seen that the difference of the\nindices is smaller compared to the difference between different materials. This means that\nthe influence of the chosen material is much larger than the influence of the injection angular\ndistribution.\n\n4 Magellan Workshop 2016\n\nLAURA VANHOEFER et al.\n\n184 Magellan Workshop 2016\n\n\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nne\nut\n\nro\nn \n\nflu\nx\n\n-310\n\n-210\n\n-110\n\n1\n\nlead\ncopper\nsteel\nLNGS rock\nconcrete\nsoil\nwater\nplastic\n\n 0MeV≥E \n\nFigure 5: Simulated neutron flux at different shielding depths normalized to the incoming\nneutron flux for different materials. The angular distribution suggested by [5] (see Eq. 1) was\nused.\n\nEnergy [MeV]\n310 410 510 610\n\nM\neV\n\ns]\n2\n\nF\nlu\n\nx \n[1\n\n/c\nm\n\n-1510\n\n-1410\n\n-1310\n\n-1210\n\n-1110\n\n-1010\n\n-910\n\n-810\n\n-710\n\n-610\n\n-510\n\nMuon Energy Spectrum (Rastin)\n\nFigure 6: Used muon energy spectrum. Datapoints are taken from [7].\n\nMagellan Workshop 2016 5\n\nNEUTRON SHIELDING SIMULATIONS AND MUON-INDUCED NEUTRONS\n\nMagellan Workshop 2016 185\n\n\n\nhist_Muminus_Copper_2m\n\nEntries  362483\nMean     1132\nRMS      2510\n\nEnergy [MeV]\n\n310 410 510 610\n\nD\niff\n\ner\nen\n\ntia\nl F\n\nlu\nx \n\n[1\n/M\n\neV\n]\n\n-1110\n\n-1010\n\n-910\n\n-810\n\n-710\n\n-610\n\n-510\n\n-410 hist_Muminus_Copper_2m\n\nEntries  362483\nMean     1132\nRMS      2510\n\n-µ\n+µ\n\n2 m  Copper\n\nFigure 7: Comparison of the µ− and µ+\n\nenergy spectrum.\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nm\nuo\n\nn \nflu\n\nx\n\n1 +µ\n-µ\n\n 0MeV≥E \n\nFigure 8: Muon flux at different shielding\ndepths normalized to the incoming muons\nfor LNGS rock.\n\n4 Muon simulations\n\nFor the muon simulations the measured energy spectrum of Rastin [7], which is shown in Fig 6,\nwas used as injection spectrum. Positive and negative charged muons were injected following\na cos2(θ) angular distribution. Figure 7 compares the energy spectra of µ+ and µ− after the\nmuons have penetrated 2m of copper. It can clearly be seen that the energy spectra of µ+ and\nµ− are very similar. Also the number of muons at different shielding depths is the same which\nis shown by Fig. 8. This is expected since they undergo similar reactions. The only difference\nis that only µ− can undergo muon capture. Low energetic µ+ can only decay and can not be\ncaptured.\nMuons induce neutrons during penetration. The number of neutrons normalized to the number\nof injected muons at different shielding depths is shown in Fig. 9. It can clearly be seen that the\nnumber of muon induced neutrons differs for µ+ and µ−. More neutrons are induced by µ−.\nFrom looking at Fig. 8 it is evident that this can not be explained by different muon shielding\nproperties, since the are the same for µ+ and µ−. The different amount of muon-induced\nneutrons can be explained by the fact that neutrons are produced during muon capture which\nonly µ− can undergo. During muon decay, no neutrons are produced.\nFrom Fig. 9 it can also be seen that the difference between µ+ and µ−-induced neutrons becomes\nsmaller at larger shielding depths, where the number of stopping muons is also smaller (see [8]).\nFigure 10 compares the energy spectra of µ+ and µ−-induced neutrons. The spectra differ since\nthey are normalized with the number of induced neutrons which is smaller for µ+. The shape\nof both energy spectra is similar up to a few MeV. Above few tens of MeV the differential fluxes\nare the same for µ+ and µ−.\nThe power law fit of Eq. 2 was applied for depths at which the shower is in equilibrium. The\nresults are shown in Table 3. As expected the shielding indices for neutrons are smaller than the\nshielding indices for muon-induced neutrons. Therefore the muon-induced neutron flux starts\nto dominate the total neutron flux at larger shielding depths. Denser material with higher mass\nnumber A shields cosmic-ray neutrons more efficiently, but also more neutrons are produced.\nTherefore the total neutron flux has to be considered.\n\n6 Magellan Workshop 2016\n\nLAURA VANHOEFER et al.\n\n186 Magellan Workshop 2016\n\n\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nne\nut\n\nro\nn \n\nflu\nx\n\n-410\n\n-310\n\n-210\n\n-110\n\n1\n\n10 -µConcrete \n+µConcrete \n\n-µCopper \n+µCopper \n\n-µLead \n+µLead \n\n-µLNGS rock \n+µLNGS rock \n\n 0MeV≥E \n\nFigure 9: Muon-induced neutrons normalized to the incoming muons at different shielding\ndepths for different materials.\n\nhist_InducedNeutrons_Muplus_Copper_2m\n\nEntries  5170\nMean   0.004788\nRMS     0.196\n\nEnergy [MeV]\n-510 -410 -310 -210 -110 1 10 210 310 410 510\n\nD\niff\n\ner\nen\n\ntia\nl F\n\nlu\nx \n\n[1\n/M\n\neV\n]\n\n-710\n\n-610\n\n-510\n\n-410\n\n-310\n\n-210\n\n-110\n\n1\n\n10\nhist_InducedNeutrons_Muplus_Copper_2m\n\nEntries  5170\nMean   0.004788\nRMS     0.196\n\n+µ\n-µ\n\nMuon-induced Neutrons at 2 m (Copper)\n\nFigure 10: Comparison of the µ−-induced and µ+-induced neutron energy spectrum.\n\nMagellan Workshop 2016 7\n\nNEUTRON SHIELDING SIMULATIONS AND MUON-INDUCED NEUTRONS\n\nMagellan Workshop 2016 187\n\n\n\nMaterial µ+ [m−1] µ− [m−1]\nPlastic −0.0193± 0.0008 −0.0246± 0.0008\nWater −0.021± 0.001 −0.0271± 0.0007\nSoil −0.0264± 0.0009 −0.045± 0.001\n\nConcrete −0.041± 0.008 −0.079± 0.004\nLNGS rock −0.038± 0.006 −0.085± 0.003\n\nSteel −0.046± 0.004 −0.065± 0.002\nCopper −0.134± 0.005 −0.256± 0.003\nLead −0.122± 0.006 −0.238± 0.002\n\nTable 3: Shielding index s for muon-induced neutrons in [1/m] (E≥ 0MeV).\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nne\nut\n\nro\nn \n\nflu\nx \n\npe\nr \n\nco\nsm\n\n. n\neu\n\ntr\non\n\n3−10\n\n2−10\n\n1−10\n\n1\n\n10\ncosm. neutrons\n\nmuon-induced\n\ntotal neutrons\n\n 0MeV≥E \n\nFigure 11: Neutron fluxes at different shielding depths for lead normalized to the incoming\ncosmic-ray neutrons. All cosmic-ray neutrons were injected vertically downwards going.\n\n5 Total neutron flux\n\nUsing the results of the cosmic-ray neutron and muon simulations, the total neutron flux can\nbe calculated. At sea-level, the ratio of neutrons to muons is 1:3 (see [9]). The muon charge\nratio is µ+/µ− ∼ 1.25 [10]. Therefore the neutron simulation results have to be weighted with\na factor of 0.25, the µ+ simulation results with a factor of 0.417 and the µ− simulation results\nwith a factor of 0.333 to get the total neutron flux.\nFigure 11 shows the total neutron flux at different shielding depths of lead. Also the cosmic-\nray and the total muon-induced neutron fluxes are shown. At smaller shielding depths, the\ntotal neutron flux is dominated by the cosmic-ray neutron flux. At larger depths, the flux is\ndominated by the muon-induced neutron flux.\nIn Fig. 12, the total neutron fluxes behind different materials is shown. Figure 13 shows the flux\nof neutrons with En ≥ 20MeV. It can be seen that the total flux is lowest behind a concrete\nshielding. This is not the case for neutrons with an energy higher than 20MeV. There are less\n\n8 Magellan Workshop 2016\n\nLAURA VANHOEFER et al.\n\n188 Magellan Workshop 2016\n\n\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nne\nut\n\nro\nn \n\nflu\nx \n\npe\nr \n\nco\nsm\n\n. n\neu\n\ntr\non\n\n-210\n\n-110\n\n1\n\nConcrete\nLNGS rock\nCopper\nLead\nSteel\nPlastic\nWater\nSoil\n\n 0MeV)≥Neutrons (E \n\nFigure 12: Total neutron flux at different shielding depths for different materials. The outgoing\nneutrons were normalized to the incoming cosmic-ray neutrons which were distributed by Eq. 1.\n\nshielding depth [m]\n0 0.5 1 1.5 2 2.5 3 3.5 4\n\nre\nl. \n\nne\nut\n\nro\nn \n\nflu\nx \n\npe\nr \n\nco\nsm\n\n. n\neu\n\ntr\non\n\n-310\n\n-210\n\n-110\n\n1\n\nConcrete\nLNGS rock\nCopper\nLead\nSteel\nPlastic\nWater\nSoil\n\n 20MeV)≥Neutrons (E \n\nFigure 13: Total neutron flux at different shielding depths for different materials. The outgoing\nneutrons were normalized to the incoming cosmic-ray neutrons which were distributed by Eq. 1.\n\nMagellan Workshop 2016 9\n\nNEUTRON SHIELDING SIMULATIONS AND MUON-INDUCED NEUTRONS\n\nMagellan Workshop 2016 189\n\n\n\nneutrons with En ≥ 20MeV after copper, lead or steel shielding. This shows that the best\nchoice of shielding material depends on the energy of neutrons which one likes to shield.\n\n6 Conclusion\n\nCosmic-ray neutron and muons (µ+ and µ−) were simulated with the GEANT4 based frame-\nwork MaGe to get the shielding powers of cosmic-ray neutrons and muon-induced neutrons\nfor different shielding materials. More neutrons are induced by µ− than by µ+ due to muon\ncapture. This difference becomes smaller at larger shielding depths, since also the number of\nstopping muons becomes smaller.\nDenser material shields more cosmic-ray neutrons, but also more neutrons are induced. There-\nfore the total neutron flux has to be calculated to compare different materials. The best choice\nof shielding material depends on the energy of neutrons which one wants to be shielded.\n\nReferences\n[1] Melissa Boswell et al. MaGe-a Geant4-based Monte Carlo Application Framework for Low-background\n\nGermanium Experiments. IEEE Trans. Nucl. Sci., 58:1212–1220, 2011.\n\n[2] H. Wulandari, J. Jochum, W. Rau, and F. von Feilitzsch. Neutron flux underground revisited. Astropart.\nPhys., 22:313–322, 2004.\n\n[3] Laura Vanhoefer et al. Neutron Shielding Simulations and Muon-induced Neutrons. PoS, NEU-\nTEL2015:085, 2015.\n\n[4] Gordon M. S. et al. Measurement of the Flux and Energy Spectrum of Cosmic-Ray Induced Neutrons on\nthe Ground. IEEE Trans. Nucl. Sci., 51:3427–3434, 2004.\n\n[5] Aleksandr Nesterenok. Numerical calculations of cosmic ray cascade in the Earth’s atmosphere: Results\nfor nucleon spectra. Nucl. Instrum. Meth., B295:99–106, 2013.\n\n[6] Aaron Michel. Investigation of cosmic ray shielding properties of different materials at shallow depths.\ndiploma thesis, Technische Universität München, 2012.\n\n[7] B. C. Rastin. AN ACCURATE MEASUREMENT OF THE SEA LEVEL MUON SPECTRUM WITHIN\nTHE RANGE 4-GEV/C TO 3000-GEV/C. J. Phys., G10:1609–1628, 1984.\n\n[8] G. L. Cassiday, J. W. Keuffel, and J. A. Thompson. Calculation of the stopping-muon rate underground.\nPhys. Rev., D7:2022–2031, 1973.\n\n[9] G. Heusser. Low-radioactivity background techniques. Ann. Rev. Nucl. Part. Sci., 45:543–590, 1995.\n\n[10] W. R. Frazer, C. H. Poon, D. Silverman, and H. J. Yesian. Limiting fragmentation and the charge ratio of\ncosmic ray muons. Phys. Rev., D5:1653–1657, 1972.\n\n10 Magellan Workshop 2016\n\nLAURA VANHOEFER et al.\n\n190 Magellan Workshop 2016\n\n\n\nThe Hamburg Observatory\n\nAfter a small refreshment at the end of the second day, all participants were invited to make a trip to the\nHamburg Observatory.\n\nMagellan Workshop 2016 191\n\n\n\n\n\nOn the history of the Hamburg Observatory\n\nStefan K.G. Dahmke1\n1Hamburger Sternwarte, Gojenbergsweg 112, D-20219 Hamburg, Germany\n\nDOI: http://dx.doi.org/10.3204/DESY-PROC-2016-05/42\n\nThis contribution aims to provide an overview of the history of the Hamburg Observatory\nfrom its early days up to the present. It highlights some of the notable individuals as\nwell as a select few projects that played major roles for astronomy and astrophysics in\nHamburg.\n\n1 Introduction\n\nDuring the Magellan Workshop 2016 in Hamburg its participants were invited to visit the Ham-\nburger Sternwarte (=Hamburg Observatory). This article is intended to give a brief summary\nof the century-long history of the observatory as well as some additional information on its cur-\nrent instruments. While this by far is not the most detailled possible analysis of the institute’s\nhistory, it hopefully provides our international visitors with a quick overview, especially since\nmost of the sources and literature so far are only available in German.\n\n2 Establishment of the observatory\n\nThe history of the Hamburg Observatory does not begin in astronomical science but instead\nin the very down-to-Earth business of surveying. The Swiss expert Johann Casper Homer was\nconducting surveys of the Weser, Elbe and Eider rivers in northern Germany in the beginning\nof the 19th century. As he tried to find suitable optical instruments to help his work, he met\nwith a certain Johann Georg Repsold, founder of a workshop for precision instruments and chief\nof the Hamburg fire brigade, who was capable of providing valuable assistance in constructing\nnew surveying equipment. Repsold’s interest in optical instruments grew rapidly and quickly\nled to an interest in astronomy. Thus he built his first own observatory in 1802, which he owned\nand operated privately for several years before French occupation under Napoleon put an end\nto it. [10]\n\nShortly after, in 1820, Repsold could convince the senate of Hamburg to grant permission\nto build a new and larger observatory at the Millerntor, not far from the city centre. Initially\nthe observatory was again a private endeavour and was largely funded by being also working as\na navigators’ school. This is a very tight link between astronomy and seafaring, which would\ncontinue to play a significant role in the history of the observatory for more than a century.\n[10][11]\n\nAfter Johann Georg Repsold, who had continued his work in the fire brigade of Hamburg,\ndied in the line of duty on January 14th 1830, the senate decided to keep the observatory and\nofficially opened it as a state intitute in 1833. Christian Carl Ludwig Rümker became the first\n\nMagellan Workshop 2016 1Magellan Workshop 2016 193\n\n\n\nFigure 1: The Millerntor observatory. [1]\n\ndirector and using a Fraunhofer refractor telescope built by the Repsold workshops - which were\nnow operated by Repsold’s sons - he published a catalogue of stars, comets and asteroids which\nearned him a gold medal from the Royal Society of London in 1854. When Rümker retired in\n1857 he, too, was succeeded by his son, who faced a major challenge in modernising the aging\nequipment of the observatory. [10]\n\nIn 1867 the observatory acquired a 26 cm refractor built on a modern equatorial mount\nfrom the Repsold company. This telescope still exists and remains functional. Despite this the\nobservatory lost influence in Germany as Georg Rümker, contrary to trends of astronomy in\nGermany, had little interest in studying galaxies and instead kept the research focus on comets,\nasteroids and astrometry. [10][11]\n\n3 Time ball\n\nAs already mentioned, there has always been a strong tie between astronomical observations\nand maritime navigation in Hamburg. This is due to two reasons, which also formed two of the\nmajor tasks for the observatory for a very long time: firstly, ships trying to fix their position\nby means of the stars need sufficiently precise stellar charts to know the coordinates of bright\nstars and consequently compare these to their own nightly observations. Secondly, to find their\ncurrent longitude, ships had to know the exact time of a reference point (like Greenwich or their\nown homeport) indicated by a clock to be compared to the culmination of the sun or bright\nstars.\n\n2 Magellan Workshop 2016\n\nSTEFAN K. G. DAHMKE\n\n194 Magellan Workshop 2016\n\n\n\nThis method of determining one’s position in longitude was introduced by the Yorkshire-\nborn carpenter and tinkerer John Harrison who not only understood how knowing the time\nat a reference point was the key to solving the longitude problem, but also managed to build\nthe first clocks that would provide this time aboard a ship with reasonable precision. Despite\nmany astronomers’ resistance - Sir Isaac Newton maintained up to his death in 1727 that it\nwas utterly impossible to build an accurate shipboard clock - it became a standard method of\nnavigation during the 19th century and again involved astronomers with the task of providing\nprecise time to ports, where ship captains would correct their shipboard clocks. [3]\n\nIn 1876 a time ball was erected in Hamburg. Figure 2 shows a technical drawing of the\nstructure. A roughly 2 m diameter leather ball was raised up in the tower and at precisely\n12:00pm GMT, 12:43 local time, dropped back down to its original position, released by an\nelectric mechanism which was actuated via telegraph from the observatory. As the time ball\nwas located on top of a large storehouse, this visual signal could be made out from all over\nthe harbour, enabling the navigators and captains to adjust their own clocks. Today a fully\nfunctional scale model built by Heinz-Otto Kult, who meticulously researched its history, is on\ndisplay at the Hamburg Observatory. [7]\n\nFigure 2: Schematic drawing of the time ball.\nThe whole structure was located on top of a\nwarehouse. [1]\n\nThe main instrument used for the determina-\ntion of time was the Meridian Circle, a type of\nspecial telescope which does not traverse but\nstrictly looks along the North-South-Meridian\nin variable altitude. The apparent movement\nof the stars as induced by the rotation of\nthe Earth then makes stars pass through the\nfield of view at different times. Noting these\ntimes and comparing them to stellar cata-\nlogues enables the observer to correct for er-\nrors in the observatory’s own clocks, of which\nseveral were in operation in a well insulated\nroom underneath the library. The advent of\nwidespread telephone and radio networks ren-\ndered the time ball less and less important\nafter the turn of the century and in 1934 it\nwas finally demolished. Despite this the pre-\ncise measurement of time remained a major\ntask for the observatory up to the 1960s, when\natomic clocks which allowed for even higher\nprecision than astronomic observations were\nestablished. The Meridian Circle in Bergedorf\nremained in use until the 1970s when it was\nshipped to Australia to take part in observa-\ntions in preparation for the Hipparcos space\nmission. [4][5][7]\n\nMagellan Workshop 2016 3\n\nON THE HISTORY OF THE HAMBURG OBSERVATORY\n\nMagellan Workshop 2016 195\n\n\n\n4 Bergedorf\n\nAs has been noted before, the influence Hamburg Observatory had on the astronomical com-\nmunity declined during the second half of the 19th century. Besides the research focus shifting\naway from general trends in Germany and Europe at the time and the fact that the observatory\nwas in need of modernising instruments and equipment, a major factor in this was the location.\nThe Millerntor site was located near the harbour, which sometimes hampered observations due\nto vibrations and smoke from the increasing number of steam ships. Also from 1882 onward\nHamburg introduced electric street lighting which further worsened the observation conditions.\n[10]\n\nTo combat these circumstances Georg Rümker and Richard Schorr, who in 1900 would\nbecome the next director, drew up plans to move the entire observatory away from the city.\nThe navigation school had already split from the observatory in the 1860s for administrative\nreasons. The current site in Bergedorf was chosen for its relatively high elevation of 40 m, its\ndistance from densely populated areas and the fact that the town of Bergedorf was already\nconnected to Hamburg via railway. After 1900 construction of several seperate buildings began\nand new instruments were ordered, making Hamburg one of Europe’s largest observatories when\nthe Bergedorf site was officially opened in 1912. Among these instruments were a new Meridian\nCircle, the Great Refractor measuring 60 cm in aperture and built by Respsold, the 1 m reflector\n(Meterspiegel) built by Carl Zeiss and the Lippert Astrograph, a 34 cm refractor built by Carl\nZeiss and donated by the local merchant Eduard Lippert. Also the 26 cm Equatorial refractor\nwas moved from the old observatory. Each of these telescopes was housed in a seperate building\nand could thus operate independently. This followed a new trend of keeping living quarters and\noffices away from the telescopes to reduce air turbulence related to heating. [6][10][11]\n\nFigure 3: The dome housing the Great Refractor during construction. [1]\n\n4 Magellan Workshop 2016\n\nSTEFAN K. G. DAHMKE\n\n196 Magellan Workshop 2016\n\n\n\nFigure 4: Arthur Arno Wachmann at the Lippert Astrograph. [1]\n\nNow equipped with state-of-the-art equipment, Hamburg observatory gained new impor-\ntance. In the 1920s multiple survey programmes began, among them the Bergedorfer Spektral-\ndurchmusterung (=Bergedorf spectral survey) by Schwassmann und Wachmann (seen in figure\n4), who used the Lippert Astrograph to obtain spectra of more than 160,000 stars. Also the As-\ntronomische Gesellschaft Katalog 2 (=Astronomical Society Catalogue 2), or AGK2 for short, a\nmajor astrometric catalogue, saw significant contributions from Bergedorf, even though it could\nnot be published until 1951 due to the outbreak of the war. In 1920 also Walter Baade joined\nthe observatory. He started out observing comets, planets and asteroids but then, towards the\nend of the 20s, became increasingly interested in the study of galaxies. [6][10]\n\nIn 1927 the probably most famous character arrived in Hamburg, when Bernhard Schmidt\naccepted an offer by Schorr to repair a horizontal telescope that Schmidt had originally built.\nThis was a favour to Schmidt who had fallen on hard times and only received a small fee plus\nfree lodging at the observatory at first. He anyway quickly made himself indispensable as a\nskilled optical engineer. [6][9][10]\n\nDuring an expedition to the Philippines in 1929, in which both Baade and Schmidt partic-\nipated, Bernhard Schmidt conceived his most famous invention: trying to tackle the problem\nof image abberations of telescopes with short focal lengths he first used a semi-spherical mir-\nror instead of the commonly found parabolic one. Then he counteracted the rising spherical\nabberation by introducing an additional optical element. The Schmidt Corrector Plate was\nan aspheric lens with convex and concave parts put in front of the mirror, at the center of\nthe mirror’s sphere shape. This lens was carefully shaped to counteract the abberation. The\nfollowing year a complete telescope based on Schmidt’s principle was built in Hamburg. The\n\nMagellan Workshop 2016 5\n\nON THE HISTORY OF THE HAMBURG OBSERVATORY\n\nMagellan Workshop 2016 197\n\n\n\nFigure 5: Bernhard Schmidt in his workshop at\nHamburg Observatory. [1]\n\nFigure 6: The original\nSchmidt telescope as seen in\n1932. [1]\n\ninstrument, shown in figure 6, immediately was a resounding success. [2][9]\n\nWhile Bernhard Schmidt himself died in 1935, telescopes of his design quickly became\npopular in astronomy. The Palomar Observatory near San Diego opened in 1936 with an\n18 in Schmidt camera. The Mount Wilson Observatory near Los Angeles began construction\nof a 1.2 m Schmidt telescope. Walter Baade, who already had hoped to work at Mt Wilson\nprior to joining the Hamburg Observatory moved to the United States in 1937, while financial\nand organisational problems delayed Hamburg’s Great Schmidt Mirror until the outbreak of\nthe war. The instrument could only be built in 1954 and then was put to use on the new\nAGK3 project, for which Bergedorf mapped most of the northern Sky. The 1.2 m Schmidt\nTelescope was operated in Hamburg until 1969, when plans to move the telescope to Calar Alto\nObservatory in Spain came up to make use of the more favourable weather there. [2][10][11]\n\nThe fact that Walter Baade, who had left Hamburg in 1937, had been Richard Schorr’s\nfavourite candidate as a successor meant that somebody else had to be named as next director.\nThere were several candidates and the decision was not very easy, as Schorr’s preference was\nOtto Heckmann, who despite having joined the nazi party and successfully having used some of\ntheir structures to further his own work in Göttingen, had fallen out of favour with the politics\ndue to his keen interest in cosmology and Einstein’s theory of relativity, which he officially\ndefended. Heckmann became director of the Hamburg Observatory in 1941 regardless. [10]\n\n5 A word on ESO\n\nOtto Heckmann as director of the Hamburg Observatory attended several international con-\nferences in the early 1950s. These conferences concerned themselves with finding ways to\nstrengthen astronomy and science in Europe in the economically difficult times. One promising\nidea was to concentrate efforts throughout Europe by starting a joint observatory, thus splitting\nthe costs of building and operating it. [8][6]\n\n6 Magellan Workshop 2016\n\nSTEFAN K. G. DAHMKE\n\n198 Magellan Workshop 2016\n\n\n\nFigure 7: Otto Heckmann (r) at the Great Schmidt Mirror 1.2 m telescope. [1]\n\nThis idea, alongside the realisation that studying our own galaxy in detail would require a\nlarge and advanced observatory in the southern hemisphere, led to the Leiden Declaration in\nJanuary 1954. This declaration states the intent of constructing a joint observatory including\na 3 m mirror telescope and a 1.2 m Schmidt camera in South Africa. It was signed by leading\nastronomers from six countries (Heckmann and Unsöld from Germany, Burgeois from Belgium,\nCouder and Danjon from France, Redman from the UK, Oort, Oosterhoff and van Rhijn from\nthe Netherlands, Lindblad, Lundmark and Malmquist from Sweden) and intended to gain more\nsupport from their respective governments. [8]\n\nScouting for a suitable location started in late 1955 and soon extended to South America,\naccounting for the troublesome political situation in South Africa. Finally, on October 5th\n1962, the European Southern Observatory was founded. From the six countries involved in\nthe Leiden Declaration five became founding members - the United Kingdom did not join.\nA month later Otto Heckmann was named as first director of ESO and offices for the newly\nfounded organisation were prepared in the town of Bergedorf, not very far form Hamburg\nObservatory, where Heckmann remained director until 1968. By that time ESO required so\nmuch of his attention, that Alfred Behr relieved him as director of the observatory. By this time\nconstruction of the ESO site in La Silla, Chile was well advanced, two telescopes exceeding 1m\nin aperture diameter were already installed. Later in the same year the journal Astronomy and\nAstrophysics, which remains one of the most influental journals for the astrophysics community,\nwas founded with the help of ESO. [8][6][10]\n\nSince Heckmann no longer had to divide his time between ESO and Bergedorf, the vicinity\nof the headquarters to Hamburg Observatory wasn’t as much of an issue anymore from 1968\n\nMagellan Workshop 2016 7\n\nON THE HISTORY OF THE HAMBURG OBSERVATORY\n\nMagellan Workshop 2016 199\n\n\n\nFigure 8: The 1-metre Telescope was the first instrument to operate at the La Silla site. Its\nfirst light was in the night of November 30th 1966. Image courtesy of ESO.\n\nonwards. Also the old offices soon proved to be inadequate in size and plans for a new ESO site\nin Europe were drawn up. The planning of the new buildings lasted until the late 1970s, when\nfinally construction began in Garching, near Munich, which not only allowed for much more\nextensive buildings, but was also easier to reach for the ever increasing number of astronomers\nfrom ever more European countries. When the Garching headquarters were finally inaugurated\nin 1981 Belgium, Denmark, France, Germany, The Netherlands and Sweden were already part\nof ESO; Italy and Switzerland joined shortly afterwards. [8]\n\nWhile Otto Heckmann retired from the post of director general of ESO in 1970, his successor\nat the Hamburg Observatory kept close ties to the international organisation. Observations from\nHamburg became less and less important, but Behr was involved in building instrumentation for\nESO, including a polarimeter. That concludes the era of major contributions of the Hamburg\nObservatory to ESO, although of course scientists from Hamburg remain involved in projects\nup until today. [8][6]\n\n6 Hamburg Observatory today\n\nHamburg Observatory has been a part of the University of Hamburg since 1968. This resulted\nin tremendous changes to the organisation of the observatory. Nowadays the majority of staff is\nmade up by students of the university who specialised their studies of physics towards astronomy\nand astrophysics.\n\nIn 1969 Prof. Alfred Weigert joined the observatory and founded a workgroup concerned\nwith stellar evolution and the modelling of the interior of stars. Both of these topics remain\nstrong suits of the Hamburg Observatory up to the present, having expanded into two seperate\ngroups. Prof. Peter Hauschildt keeps expanding on the renowned stellar atmosphere simulation\n\n8 Magellan Workshop 2016\n\nSTEFAN K. G. DAHMKE\n\n200 Magellan Workshop 2016\n\n\n\ncode PHOENIX while Prof. Robi Banarjee continues work on stellar evolution.\n\nSoon after, in 1972, Prof. Heinrich Wendker founded his workgroup on extragalactic as-\ntrophysics and made efforts to extend observations to both radio and X-ray wavelengths, the\nlatter of which of course had to be conducted from space. The extragalactic work later devel-\noped into one of the major achievements of the 1980s, the Hamburg Quasar Survey, for which\nphotographic plates taken with the Great Schmidt Mirror at Calar Alto as part of the All\nSky Objective PrismSurvey were scanned and analysed with the help of computers. Work on\nextragalactic astrophysics is still ongoing under direction of Prof. Marcus Brüggen and Prof.\nAnnalisa Bonafede, including the observatory’s involvement in the LOw Frequency ARray, LO-\nFAR, which consists of more than 48 stations across all of Europe.\n\nObservations in wavelengths ranging from infrared to X-rays continue both in utilising Berge-\ndorf’s own telescopes and analysing data obtained at other observatories in the workgroup of\nProf. Jürgen Schmitt, who specialised in stellar activity as well as characterisation of exoplanets\nthrough spectroscopy and photometry.\n\nThe work of Prof. Alfred Behr and others before him in designing new astronomical instru-\nments is currently being carried on by Prof. Günter Wiedemann, who especially concentrates\non spectroscopy and solar physics, as well as observations in optical and infrared wavelengths.\nFor these projects a seperate 70 cm telescope, designated G1, has recently been set up at Berge-\ndorf.\n\nThe Great Schmidt Mirror, which was shipped to Spain, left Hamburg with an empty tele-\nscope dome. In 1975 the new Oskar Lühning Teleskop commenced observations. The 1.2 m\nmirror telescope remains the primary telescope for scientific observations in Hamburg today, in\nno small part thanks to a major effort of modernising the control system. The whole telescope\nis computer controlled, can be remotely operated and features modern CCD detectors since\n2000.\n\nAlso in the mid-1970s the Lippert Astrograph underwent major changes and now features\na 60 cm aperture mirror, effectively matching the Great Refractor. The telescope is manually\ncontrolled and can be used for both visual observations and camera exposures. While it’s only\nused for scientific observations occasionally, it is used extensively for teaching purposes as well\nas for public outreach.\n\nThe historic Great Refractor, the Meterspiegel 1 m mirror telescope and the Equatorial all\nremain in varying degrees of restoration. The mirror telescope has widely been refurbished in\nrecent years. A project to restore the Great Refractor is currently in planning, as the telescope\nitself is in fully operational condition, but the building around it and especially the dome\nmechanics are in bad repair prohibiting any use of the instrument. The Equatorial is still\nfunctional after almost 150 years, but has a seriously impaired field of view due to nearby trees.\nAll three of these instruments are in no scientific use anymore and are mostly of historic and\npublic interest today.\n\nReferences\n\n[1] Photographic Archive of the Hamburg Observatory\n\n[2] Mark Christensen, Schmidt - The Man and the Camera, The Fox Valley Astronomical\nSociety, 2000\n\nMagellan Workshop 2016 9\n\nON THE HISTORY OF THE HAMBURG OBSERVATORY\n\nMagellan Workshop 2016 201\n\n\n\n[3] Rupert T. Gould, John Harrison and his Timekeepers, Lecture recorded in The Mariner’s\nMirror Volume 21 Issue 2, 1935\n\n[4] Erik Høg, Astrometry lost and regained - from a modest experiment in Copenhagen in 1925\nto the Hipparcos and Gaia space missions, Lecture held at Kroppedal Museum, 2010\n\n[5] Erik Høg, private communication\n\n[6] Jahresberichte Hamburger Sternwarte, Annual Reports of the Hamburg Observatory\n\n[7] Heinz-Otto Kult, private communication\n\n[8] Official ESO Timeline, http://www.eso.org/public/about-eso/timeline/\n\n[9] Richard Schorr, Bernhard Schmidt †, Astronomische Nachrichten, 1936\n\n[10] Jochen Schramm, Sterne über Hamburg, 2nd edition, Initiative zum Erhalt historischer\nBauten, 2010\n\n[11] G. Wolfschmidt, A. Seemann, D. Kühl, Hamburger Sternwarte - Geschichte und Erhaltung,\nFörderverein der Hamburger Sternwarte e.V., 2001\n\n10 Magellan Workshop 2016\n\nSTEFAN K. G. DAHMKE\n\n202 Magellan Workshop 2016\n\n\n\nFigure 1: Hamburg Observatory seen from the air in the late 1920s. Aerial photography was a\nrare event and it was decided to open all domes for the occasion. Credit: Photographic Archive\nof the Hamburg Observatory\n\n\n\nFigure 2: The Great Refractor dome under construction in 1910. Credit: Photographic Archive\nof the Hamburg Observatory\n\n\n\nFigure 3: Construction work on the mount for the Great Refractor telescope in March 1910.\nCredit: Photographic Archive of the Hamburg Observatory\n\n\n\nFigure 4: Walter Baade inside the dome of the 1912-built 1m mirror telescope in Hamburg.\nCredit: Photographic Archive of the Hamburg Observatory\n\n\n\nFigure 5: The Beamtenwohnhaus (=Officials’ Quarters) in which up to four astronomers and\ntheir families used to live. Despite ever improving connections to public transport and cars\nbecoming widespread, employees lodged here up until the mid-1980s. Today the building is\nused as office space for the observatory. Credit: S. Dahmke\n\n\n\nFigure 6: The Meridian Circle building today. The instrument built to determine the exact\ntime used to be one of the most important ones for the Hamburg Observatory, yet today the\nbuilding is empty. The telescope itself is currently stored in a museum in Munich. Credit: S.\nDahmke\n\n\n\nFigure 7: The dome of the Oskar Lühning Teleskop. The 1.2m telescope is Hamburg’s primary\nscience telescope housed in a dome originally conceived for the Great Schmidt Mirror. Credit:\nS. Dahmke\n\n\n\nFigure 8: The ESO 1m Schmidt telescope which became operational in December 1971. Oscar\nPizarro is adjusting the plateholder. Credit: ESO\n\n\n\nFigure 9: Painting of Bernhard Schmidt, inventor of the Schmidt Mirror, by Erik Schmidt.\n\n\n\nFigure 10: Painting of Richard Schorr, Director of Hamburg Observatory from 1900 to 1941.\nCredit: Hamburg Observatory\n\n\n\nFigure 11: Painting of Prof. Otto Heckmann, Director of Hamburg Observatory from 1941 to\n1968, ESO Director General from 1962 to 1969. Credit: ESO\n\n\n\nFigure 12: On June 8-10 1963, ESO officials were the guestes of AURA on their property and\non June 10 gathered on Cerro Morado, discussing ESO prospects in Chile. On June 8 1963,\nthey undertook the trip on horseback to Tololo, where most of June 9 was spent for inspection\nof the AURA site. From there, both groups went on June 10 to the neighbouring mountain\nMorado on the AURA territory, south of Tololo; it was AURA’s suggestion that this mountain,\nwith its large surface and well tested, favourable observing conditions, might offer a suitable\nlocation for the ESO Observatory. On the photograph, from left to right: Ch. Fehrenbach, O.\nHeckmann, Sr Marchetti, J.H. Oort, N.U. Mayall, F.J. Edmondson and A.B. Muller. Credit:\nESO/F.K.Edmondson\n\n\n\nC\nan\n\nar\nyg\n\nIs\nla\n\nn\nd\n\ns\n\nS\nan\n\nlú\nca\n\nrg\nd\n\neg\nB\n\nar\nra\n\nm\ned\n\na\n\nC\nap\n\neg\nV\n\ne\nrd\n\neg\nIs\n\nla\nn\n\nd\ns\n\nS\nan\n\nta\ngL\n\nu\nci\n\nag\nB\n\nay\n-R\n\nio\ngd\n\neg\nJa\n\nn\nei\n\nro\ngB\n\nay\nF\n\nS\nep\n\nte\nm\n\nb\ner\n\nF2\n0\ndF\n1\n5\n1\n9\n\nS\nep\n\nte\nm\n\nb\ner\n\nF6\ndF\n1\n5\n2\n2\n\nS\nep\n\nte\nm\n\nb\ner\n\nF2\n6\ndF\n1\n5\n1\n9\n\nJu\nly\n\nF9\ndF\n1\n5\n2\n2\n\nN\no\nve\n\nm\nb\ner\n\nF2\n9\ndF\n1\n5\n1\n9\n\nD\nec\n\nem\nb\ner\n\nF1\n3\ndF\n1\n5\n1\n9\n\nJa\nn\nu\na\nry\n\nF1\n2\ndF\n1\n5\n2\n0 M\na\nrc\n\nh\nF3\n\n1\ndF\n1\n5\n2\n0\n\nO\nct\n\no\nb\ner\n\nF2\n1\ndF\n1\n5\n2\n0\n\nN\no\nve\n\nm\nb\ner\n\nF2\n8\ndF\n1\n5\n2\n0\n\nJa\nn\nu\na\nry\n\nF2\n1\ndF\n1\n5\n2\n1\n\nF\neb\n\nru\na\nry\n\nF4\ndF\n1\n5\n2\n1\n\nM\na\nyF\n\n1\n9\ndF\n1\n5\n2\n2\n\nM\na\nrc\n\nh\nF1\n\n6\ndF\n1\n5\n2\n1\n\nM\na\nrc\n\nh\nF6\n\ndF\n1\n5\n2\n1\n\nM\na\nrc\n\nh\nF1\n\n7\ndF\n1\n5\n2\n1\n\nM\na\nrc\n\nh\nF2\n\n8\ndF\n1\n5\n2\n1\n\nA\np\nri\n\nlF\n2\n7\ndF\n1\n5\n2\n1\n\nA\np\nri\n\nlF\n7\ndF\n1\n5\n2\n1 N\n\no\nve\n\nm\nb\ner\n\nF8\ndF\n1\n5\n2\n1\n\nD\nec\n\nem\nb\ner\n\nF2\n9\ndF\n1\n5\n2\n1\n\nJa\nn\nu\na\nry\n\nF2\n5\ndF\n1\n5\n2\n2\n\nR\nío\n\ngd\neg\n\nS\no\n\nlis\n-R\n\nío\ngd\n\neg\nla\n\ngP\nla\n\nta\nF\n\nP\nu\n\ner\nto\n\ngS\nan\n\ngJ\nu\n\nliá\nn\n\nC\nab\n\no\ngV\n\nir\ng\n\nen\nes\n\n-C\nap\n\ne\ngV\n\nir\ng\n\nen\nes\n\nF\n\nA\nllg\n\nS\nai\n\nn\nts\n\ngS\ntr\n\nai\nt\n\n-S\ntr\n\nai\ntg\n\no\nfg\n\nM\nag\n\nel\nla\n\nn\nF\n\nC\nab\n\no\ngD\n\nes\nea\n\nd\no\n\nS\nh\n\nar\nks\n\n'gI\nsl\n\nan\nd\n\ns\n-P\n\nu\nk\n\naG\nP\n\nu\nka\n\nF\n\nS\nan\n\ngP\na\n\nb\nlo\n\ngIs\nla\n\nn\nd\n\n-V\no\n\ns\nto\n\nkg\nIs\n\nla\nn\n\nd\ngo\n\nrg\nF\n\nlin\ntg\n\nIs\nla\n\nn\nd\n\nF\n\nL\nad\n\nro\nn\n\nes\ngIs\n\nla\nn\n\nd\ns\n\n-M\nar\n\nia\nn\n\na\ngIs\n\nla\nn\n\nd\nsF\n\nP\nal\n\naw\nan\n\nB\nru\n\nn\nei\n\nT\nid\n\no\nre\n\nA\nm\n\nb\no\n\nn\ngIs\n\nla\nn\n\nd\n\nT\nim\n\no\nr\n\nS\nam\n\nar\n\nH\no\n\nm\no\n\nn\nh\n\no\nn\n\nM\nac\n\nta\nn C\neb\n\nu\n\nL\nim\n\nas\naw\n\na\n\nF\nor\n\nm\ner\n\n n\nam\n\ned\nC\n\nab\no\n\ngD\nes\n\nea\nd\n\no\n\nM\nod\n\ner\nn \n\nna\nm\n\ne\n-M\n\nar\nia\n\nn\na\n\ngIs\nla\n\nn\nd\n\nsF\n\nA\nrr\n\niv\nal\n\nor\n p\n\nas\nsi\n\nn\ng \n\nth\nru\n\n d\nat\n\ne\nS\nep\n\nte\nm\n\nb\ner\n\nF2\n6\ndF\n1\n5\n1\n9\n\nC\nap\n\neg\no\n\nfg\nG\n\no\no\n\nd\ngH\n\no\np\n\ne\n\nP\nA\n\nC\nIF\n\nIC\nO\n\nC\nE\n\nA\nN\n\nA\nT\n\nL\nA\n\nN\nT\n\nIC\nO\n\nC\nE\n\nA\nN\n\nIN\nD\n\nIA\nN\n\nO\nC\n\nE\nA\n\nN\n\nP\nA\n\nC\nIF\n\nIC\nO\n\nC\nE\n\nA\nN\n\nM\na\ng\nel\n\nla\nn\n\na\nn\nd\nFE\n\nlc\na\nn\no\n\nci\nrc\n\nu\nm\n\nn\na\nvi\n\ng\na\nti\n\no\nn\n\nJu\na\nn\nFS\n\neb\na\nst\n\niá\nn\nFE\n\nlc\na\nn\no\nF\n\n2\n0\n/0\n\n9\n/1\n\n5\n1\n9\n\n0\n6\n/0\n\n9\n/1\n\n5\n2\n2\n\nM\nag\n\nel\nla\n\nn\n\nE\nlc\n\nan\no\n\nP\nas\n\nsi\nng\n\n b\ny\n\nM\nag\n\nel\nla\n\nn'\ns \n\nde\na\n\nth\n\nS\nto\n\npo\nve\n\nr\n\nFigure 13: Map of the Magellan-Elcano voyage. Victoria, one of the original five ships, circum-\nnavigated the globe and returned to Spain 16 months after Magellan’s death. Credit: Atelier\nGraphique\n\n\n\nFigure 14: An anonymous painting of Ferdinand Magellan, 16th or 17th century (The Mariner’s\nMuseum Collection, Newport News, VA).\n\n\n\nFigure 15: Photographic image of the Large Magellanic Cloud, before (left) and after (right)\nthe explosion of SN1987A. The supernova is visible on the right image just below the Tarantula\nnebula, in the upper part of the irregular galaxy. Credit: ESO\n\nFigure 16: Large Magellanic Cloud and Small Magellanic Cloud. Credit: ESO/S. Brunier\n\n\n\nFigure 17: Image obtained with the ESO Schmidt Telescope of the Tarantula Nebula in the\nLarge Magellanic Cloud. Supernova 1987A is clearly visible as the very bright star in the middle\nright. At the time of this image, the supernova was visible with the unaided eye. Credit: ESO\n\n\n\nFigure 18: Solar flares. Credit: NASA\n\n\n\nFigure 19: Exterior view of the Borexino detector located at Laboratori Nazionali del Gran\nSasso. Credit: Borexino collaboration\n\n\n\nFigure 20: Interior view of the Borexino detector. Credit: Borexino collaboration\n\n\n\nFigure 21: Hamburg in March of 2016. View over the Binnenalster. Credit: L. Vanhoefer\n\n\n\nList of Authors\n\nAbt, I., 181\nAgostini, M., 105, 175\nAltenmüller, K., 105, 175\nAppel, S., 105, 175\nAstapov, I., 135, 143\n\nBarrios-Martı́, J., 19\nBellini, G., 105, 175\nBenziger, J., 105, 175\nBerton, N., 105, 175\nBezyazeekov, P., 135, 143\nBick, D., 105, 175\nBogdanov, A., 135, 143\nBonfini, G., 105, 175\nBoreyko, V., 135, 143\nBrückner, M., 135, 143\nBravo, D., 105, 175\nBudnev, N., 135, 143\n\nCaccianiga, B., 105, 175\nCalaprice, F., 105, 175\nCaminata, A., 105, 175\nCarlini, M., 105, 175\nCavalcante, P., 105, 175\nCavanna, F., 129\nChepurnov, A., 105, 175\nChiavassa, A., 135, 143\nChoi, K., 105, 175\nChvalaev, O., 135, 143\nClifton, G. A., 55\nCribier, M., 105, 175\n\nD’Angelo, D., 105, 175\nDahmke, S. K. G., 193\nDavini, S., 35, 105, 175\nDerbin, A., 105, 175\nDi Noto, L., 105, 175\nDrachnev, I., 105, 175\nDurero, M., 105, 175\nDyachok, A., 135, 143\n\nEpimakhov, S., 135, 143\nEtenko, A., 105, 175\n\nFarinon, S., 105, 175\nFedorov, O., 135, 143\nFeilitzsch, F. von, 105, 175\nFischer, T., 11\nFischer, V., 105, 175\nFomenko, K., 105, 175\nFormozov, A., 105, 175\nFranco, D., 105, 113, 175\n\nGabriele, F., 105, 175\nGafarov, A., 135, 143\nGaffiot, J., 105, 175\nGalbiati, C., 105, 175\nGarbini, L., 181\nGhiano, C., 105, 175\nGiammarchi, M., 105, 175\nGlawion, D., 151\nGoeger-Neff, M., 105, 175\nGorbunov, N., 135, 143\nGoretti, A., 105, 175\nGrebenyuk, V., 135, 143\nGress, O., 135, 143\nGress, T., 135, 143\nGrinuk, A., 135, 143\nGrishin, O., 135, 143\nGromov, M., 105, 175\n\nHagner, C., 105, 175\nHorns, D., 135, 143\nHoudy, Th., 105, 175\nHungerford, E., 105, 175\n\nIanni, Aldo, 105, 175\nIanni, Andrea, 105, 175\nIvanova, A., 135, 143\n\nJedrzejczak, K., 105, 175\n\nMagellan Workshop 2016 223\n\n\n\nJeschke, D., 105, 175\nJonquères, N., 105, 175\n\nKaiser, M., 105, 175\nKalinin, A., 135, 143\nKalmykov, N., 135, 143\nKarpov, N., 135, 143\nKazarina, Y., 135, 143\nKirichkov, N., 135, 143\nKiryuhin, S., 135, 143\nKobychev, V., 105, 175\nKokoulin, R., 135, 143\nKomponiest, K., 135, 143\nKonstantinov, A., 135, 143\nKorablev, D., 105, 175\nKorga, G., 105, 175\nKornoukhov, V., 105, 175\nKorosteleva, E., 135, 143\nKozhin, V., 135, 143\nKryn, D., 105, 175\nKunnas, M., 135, 143\nKuzmichev, L., 135, 143\n\nLachenmaier, T., 105, 175\nLasserre, T., 105, 175\nLaubenstein, M., 105, 175\nLehnert, B., 105, 175\nLeibundgut, B., 165\nLenok, V., 135, 143\nLiao, H.-Y., 181\nLink, J., 105, 175\nLitvinovich, E., 105, 175\nLohs, A., 11\nLombardi, F., 105, 175\nLombardi, P., 105, 175\nLorenz, S., 121\nLozza, V., 87\nLubsandorzhiev, B., 135, 143\nLubsandorzhiev, N., 135, 143\nLudhova, L., 105, 157, 175\nLukyanchenko, G., 105, 175\n\nMachulin, I., 105, 175\nMajorovits, B., 181\nManecki, S., 105, 175\nManeschg, W., 105, 175\nMarcocci, S., 95, 105, 175\nMaricic, J., 105, 175\nMartinez-Pinedo, G., 11\n\nMauri, N., 47\nMention, G., 105, 175\nMeroni, E., 105, 175\nMeyer, M., 105, 175\nMiloradovic, M., 41\nMiramonti, L., 105, 175\nMirgazov, R., 135, 143\nMirzoyan, R., 135, 143\nMisiaszek, M., 105, 175\nMonkhoev, R., 135, 143\nMontuschi, M., 105, 175\nMosteiro, P., 105, 175\nMuratova, V., 105, 175\nMusenich, R., 105, 175\n\nNachtigall, R., 135, 143\nNeumair, B., 105, 175\n\nOberauer, L., 105, 175\nObolensky, M., 105, 175\nOrtica, F., 105, 175\nOsipova, E., 135, 143\n\nPakhorukov, A., 135, 143\nPalermo, M., 181\nPallavicini, M., 105, 175\nPanasyuk, M., 135, 143\nPankov, L., 135, 143\nPaoletti, D., 71\nPapp, L., 105, 175\nPerasso, L., 105, 175\nPetrukhin, A., 135, 143\nPlatonov, V., 135, 143\nPocar, A., 105, 175\nPoleschuk, V., 135, 143\nPopova, E., 135, 143\nPorelli, A., 135, 143\nPostnikov, E., 135, 143\nProsin, V., 135, 143\nPtuskin, V., 135, 143\nPushnin, A., 135, 143\n\nRanucci, G., 105, 175\nRazeto, A., 105, 175\nRe, A., 105, 175\nRomani, A., 105, 175\nRoncin, R., 105, 175\nRossi, N., 105, 175\nRubtsov, G., 135, 143\n\n224 Magellan Workshop 2016\n\n\n\nSamoliga, V., 135, 143\nSatunin, P., 135, 143\nSaviano, N., 3\nSchönert, S., 105, 175\nSchulz, O., 181\nScola, L., 105, 175\nSemeney, Yu., 135, 143\nSemenov, D., 105, 175\nSerenelli, A., 27\nSilaev (junior), A., 135, 143\nSilaev, A., 135, 143\nSimgen, H., 105, 175\nSkorokhvatov, M., 105, 175\nSkurikhin, A., 135, 143\nSlucka, V., 135, 143\nSmirnov, O., 105, 175\nSotnikov, A., 105, 175\nSpiering, C., 135, 143\nSukhotin, S., 105, 175\nSuvorov, R., 105, 175\nSveshnikova, L., 135, 143\n\nTabolenko, V., 135, 143\nTarashansky, B., 135, 143\nTartaglia, Y., 105, 175\nTestera, G., 105, 175\nThurn, J., 105, 175\nTkachenko, A., 135, 143\nTkachev, L., 135, 143\nTluczykont, M., 135, 143\nToropova, M., 105, 175\n\nUnzhakov, E., 105, 175\n\nVanhoefer, L., 181\nVeyssiére, C., 105, 175\nVihonen, S., 65\nVinyoles, N., 27\nVivier, M., 105, 175\nVogelaar, R. B., 105, 175\nVoronin, D., 135, 143\n\nWang, H., 105, 175\nWeinz, S., 105, 175\nWinter, J., 105, 175\nWischnewski, R., 135, 143\nWojcik, M., 105, 175\nWurm, M., 105, 175\n\nYashin, I., 135, 143\nYokley, Z., 105, 175\n\nZagorodnikov, A., 135, 143\nZaimidoroga, O., 105, 175\nZavatarelli, S., 105, 175\nZuber, K., 105, 175\nZurbanov, V., 135, 143\nZuzel, G., 105, 175\n\nMagellan Workshop 2016 225\n\n\n\n\n\nParticipants\n\nAhlgren, Björn\nBarrios Martı́, Javier\nBüttner, Benjamin\nCaminata, Alessio\nCavanna, Francesca\nClifton, Alex\nDahmke, Stefan\nFranco, Davide\nFuessling, Matthias\nGlawion, Dorit\nHeisel, Mark\nHekker, Saskia\nHollnagel, Annika\nIzaguirre, Ignacio\nJames, Clancy\nKneissl, Raphael\nKowalski, Marek\nKunnas, Maike\nLarsson, Josefin\nLeibundgut, Bruno\nLohs, Andreas\nLorenz, Sebastian\nLozza, Valentina\nLudhova, Livia\nMarcocci, Simone\nMauri, Nicoletta\nMeyer, Mikko\nMeyhöfer, David\nMiloradovic, Michael\nMüller, Cornelia\nOpitz, Björn\nPaoletti, Daniela\nRanitzsch, Philipp Chung-On\nRebber, Henning\nSaviano, Ninetta\nSerenelli, Aldo\nStrotjohann, Nora Linn\nTamborra, Irene\nTluczykont, Martin\nVanhoefer, Annika\nVanhoefer, Laura\nVihonen, Sampsa\nVoge, Markus\nVovk, Ievgen\nYanez Garza, Juan Pablo\n\nMagellan Workshop 2016 227\n\n\n\nSteure, mutiger Segler! Es mag der Witz dich verhöhnen,\nUnd der Schiffer am Steur senken die lässige Hand.\nImmer, immer nach West! Dort muß die Küste sich zeigen,\nLiegt sie doch deutlich und liegt schimmernd vor deinem Verstand.\n\nTraue dem leitenden Gott und folge dem schweigenden Weltmeer,\nWär sie noch nicht, sie stieg jetzt aus den Fluten empor.\nMit dem Genius steht die Natur in ewigem Bunde,\nWas der eine verspricht, leistet die andre gewiß.\n\n- Kolumbus (Friedrich Schiller)\n\nSteer, bold mariner, on! albeit witlings deride thee,\nAnd the steersman drop idly his hand at the helm;\nEver, ever to westward! There must the coast be discovered,\nIf it but lie distinct, luminous lie in thy mind.\n\nTrust to the God that leads thee, and follow the sea that is silent;\nDid it not yet exist, now would it rise from the flood.\nNature with Genius stands united in league everlasting;\nWhat is promised to one, surely the other performs.\n\n- Columbus (by Friedrich Schiller, translated by Henry Wadsworth Longfellow)\n\n228 Magellan Workshop 2016"},"fulltext":true,"key":"357195b6adc92a693a72ec7c49bb90df","url":"https://inspirehep.net/files/357195b6adc92a693a72ec7c49bb90df"}],"isbns":[{"value":"9783945931080"}],"$schema":"https://inspirehep.net/schemas/records/hep.json","references":[{"reference":{"label":"1","publication_info":{"journal_volume":"39","artid":"2","year":2016,"page_start":"2","journal_title":"Riv.Nuovo Cim."},"misc":["1"],"authors":[{"full_name":"Mirizzi, A."},{"full_name":"Tamborra, I."},{"full_name":"Janka, H.T."},{"full_name":"Saviano, N."},{"full_name":"Scholberg, 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Seixas"],"authors":[{"full_name":"Chakraborty, S."},{"full_name":"Mirizzi, A."},{"full_name":"Saviano, N."}]},"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/1280669"}},{"reference":{"dois":["10.3204/DESY-PROC-2016-05/28"],"label":"17","publication_info":{"year":2016},"misc":["8 Magellan WorkshopNINETTA SAVIANO 10 Magellan WorkshopNeutrino-Nucleon Interactions in Core Collapse Supernovae Andreas Lohs1, Gabriel Martinez-Pinedo2,3, Tobias Fischer 4 1 Universität Basel, Departement Physik, Klingelbergstraße 82, 4056 Basel, Switzerland 2 Technische Universität Darmstadt, Schlossgartenstraße 2, 64289 Darmstadt, Germany 3 GSI Helmholtzzentrum für Schwerionenforschung, Planckstraße 1, 64291 Darmstadt, Germany 4 Institute for Theoretical Physics, University of Wroclaw, plac Maksa Borna 9, 50-204 Wroclaw, Poland DOI: Neutrino interactions in proto-neutron star matter are a major determinant for many key properties of core-collapse supernovae (CCSNe), such as the explosion mechanism or the nucleosynthesis of heavy elements. Recent works identify a need for increasingly precise interaction rates. 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The documents may come from\nteaching and research institutions in France or\nabroad, or from public or private research centers.\n\nL’archive ouverte pluridisciplinaire HAL, est\ndestinée au dépôt et à la diffusion de documents\nscientifiques de niveau recherche, publiés ou non,\némanant des établissements d’enseignement et de\nrecherche français ou étrangers, des laboratoires\npublics ou privés.\n\nStudy of planar pixel sensors hardened to radiations for\nthe upgrade of the ATLAS vertex detector\n\nMathieu Benoit\n\nTo cite this version:\nMathieu Benoit. Study of planar pixel sensors hardened to radiations for the upgrade of the ATLAS\nvertex detector. Other [cond-mat.other]. Université Paris Sud - Paris XI, 2011. English. �NNT :\n2011PA112070�. �tel-00610015v2�\n\nhttps://tel.archives-ouvertes.fr/tel-00610015v2\nhttps://hal.archives-ouvertes.fr\n\n\nLAL $$%$$& \nMai 2011 \n\n \n \n \n \n \n \n\nTHÈSE \n \n \n\nPrésentée le 10 juin 2011 \n \n\npar \n \n\nMathieu BENOIT \n \n \n\npour obtenir le grade de \n \n\nDocteur ès Sciences \nde l’Université Paris XI, Orsay \n\n \n \n \n \n\nÉtude des détecteurs planaires pixels durcis aux \nradiations pour la mise à jour du détecteur \n\nde vertex d’ATLAS \n \n \n\n \n \n \n \n \n \n\nSoutenue devant la commission d’examen composée de : \n \n\nM. E. Augé  \nM. G Casse Rapporteur \nM. C. Goessling Rapporteur \nM. L.-A. Hamel  \nM. A. Lounis Directeur de thèse \nM. A. Stocchi Président \n\n\n\n\"I’ve found from past experiences that the tighter your plan, the more likely you are to\nrun into something unpredictable.\"\n\nMacGyver\n\n\n\n\n\n4\n\n\n\nRésumé\n\nLe Large Hadron Collider (LHC), située au CERN, Genève, produit des collisions de\n\nprotons accélérés à une énergie de 3.5 TeV depuis le 23 Novembre 2009. L’expérience\n\nATLAS enregistre depuis des données et poursuit sa recherche de nouvelle physique à\n\ntravers l’analyse de la cinématique des événements issues des collisions. L’augmentation\n\nprévue de la luminosité sur la période s’étalant de 2011 2020 apportera de nouveaux\n\ndéfis pour le détecteur qui doivent être considérés pour maintenir les bonnes performance\n\nde la configuration actuelle. Le détecteur interne sera le sous-détecteur le plus affecté\n\npar l’augmentation de la luminosité qui se traduira par une augmentation des dommages\n\noccasionés par la forte radiation et par la multiplication du nombre de traces associées à\n\nchaque croisement de faisceau. Les dommages causés par l’irradiation intense entrainera\n\nune perte d’efficacité de détection et une réduction du nombre de canaux actifs.\n\nUn intense effort de Recherche et Developpement (R&D) est présentement en cours\n\npour concevoir un nouveau détecteur pixel plus tolérant aux radiations et au cumul des\n\névénements générant un grand nombre de traces à reconstruire. Un premier projet de mise-\n\nà-jour du détecteur interne, nommé Insertable B-Layer (IBL) consiste à ajouter un couche\n\nde détection entre le tube à vide du faisceau et la première couche de silicium. Le projet\n\nSLHC prévoit de remplacer l’ensemble du détecteur interne par une version améliorée plus\n\ntolérante aux radiations et aux cumuls des événements. Dans cet ouvrage, je présente\n\nune étude utilisant la simulation technologique assisté par ordinateur (TCAD) portant\n\nsur les méthodes de conception des détecteurs pixels planaires permettant de réduire les\n\nzones inactives des détecteurs et d’augmenter leurs tolérances aux radiations. Les différents\n\nmodèles physiques disponible ont étés étudiés pour développer un modèle cohérent capable\n\nde prédire le fonctionnement des détecteurs pixels planaires après irradiation. La structure\n\n5\n\n\n\nd’anneaux de gardes utilisée dans le détecteur interne actuel a été étudié pour obtenir de\n\nl’information sur les possible méthodes permettant de réduire l’étendu de la surface occupée\n\npar cette structure tout en conservant un fonctionnement stable tout au long de la vie du\n\ndétecteur dans l’expérience ATLAS. Une campagne de mesures sur des structures pixels fut\n\norganisée pour comparer les résultats obtenue grâce à la simulation avec le comportement\n\ndes structures réelles. Les paramètres de fabrication ainsi que le comportement électrique\n\nont été mesurés et comparés aux simulations pour valider et calibrer le modèle de simulation\n\nTCAD. Un modèle a été développé pour expliquer la collection de charge excessive observée\n\ndans les détecteurs planaires en silicium lors de leur exposition a une dose extrême de\n\nradiations.\n\nFinalement, un modèle simple de digitalisation à utiliser pour la simulation de per-\n\nformances detecteurs pixels individuels exposès à des faisceau de hauteènergie ou bien de\n\nl’ensemble du détecteur interne est présenté. Ce modèle simple permets la comparaison\n\nentre les données obtenue en faisceau test aux modèle de transport de charge inclut dans la\n\ndigitalisation. Le dommage dû à la radiation , l’amincissement et l’utilisation de structures\n\nà bords minces sont autant de structures dont les effets sur la collecte de charges affectent\n\nles performance du détecteur. Le modèle de digititalisation fut validé pour un détecteur\n\nnon-irradié en comparant les résultats obtenues avec les données acquises en test faisceau\n\nde hauténergie. Le modèle validé sera utilisé pour produire la première simulation de l’IBL\n\nincluant les effets d’amincissement du substrat, de dommages dûes aux radiations et de\n\nstructure dotés de bords fins.\n\nKeywords : Dommage induit par la radiation, silicium, détecteur pixel planaires, simu-\n\nlation TCAD, test faisceau , IBL, SLHC\n\n6\n\n\n\n\n\n8\n\n\n\nAbstract\n\nIn this work, is presented a study, using TCAD simulation, of the possible methods of\n\ndesigning of a planar pixel sensors by reducing their inactive area and improving their\n\nradiation hardness for use in the Insertable B-Layer (IBL) project and for SLHC upgrade\n\nphase for the ATLAS experiment. Different physical models available have been studied\n\nto develop a coherent model of radiation damage in silicon that can be used to predict\n\nsilicon pixel sensor behavior after exposure to radiation. The Multi-Guard Ring Structure,\n\na protection structure used in pixel sensor design was studied to obtain guidelines for the\n\nreduction of inactive edges detrimental to detector operation while keeping a good sensor\n\nbehavior through its lifetime in the ATLAS detector. A campaign of measurement of the\n\nsensor’s process parameters and electrical behavior to validate and calibrate the TCAD\n\nsimulation models and results are also presented. A model for diode charge collection in\n\nhighly irradiated environment was developed to explain the high charge collection observed\n\nin highly irradiated devices.\n\nA simple planar pixel sensor digitization model to be used in test beam and full detector\n\nsystem is detailed. It allows for easy comparison between experimental data and prediction\n\nby the various radiation damage models available. The digitizer has been validated using\n\ntest beam data for unirradiated sensors and can be used to produce the first full scale\n\nsimulation of the ATLAS detector with the IBL that include sensor effects such as slim\n\nedge and thinning of the sensor.\n\nKeywords : Radiation Damage, Multi-Guard Ring Structure, Silicon detector, TCAD\n\nsimulation, Digitization, Planar Pixel Sensor, Slim edges, Test Beam, IBL, SLHC\n\n9\n\n\n\n\n\nTable des matières\n\nIntroduction 15\n\n1 The ATLAS experiment and upgrade project 17\n\n1.1 The Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . . . . . . 17\n\n1.2 The ATLAS experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19\n\n1.2.1 The Inner detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20\n\n1.2.2 The calorimeter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25\n\n1.2.3 The muon spectrometer . . . . . . . . . . . . . . . . . . . . . . . . . 29\n\n1.3 THe ATLAS upgrade projects . . . . . . . . . . . . . . . . . . . . . . . . . . 30\n\n1.3.1 Phase 1 : The Insertable B-Layer (IBL) . . . . . . . . . . . . . . . . 32\n\n1.3.2 Phase 2 : Upgrade for high luminosity . . . . . . . . . . . . . . . . . 35\n\n2 Principles of Silicon pixel sensors 39\n\n2.1 The physics of Silicon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40\n\n2.1.1 Semiconductors properties . . . . . . . . . . . . . . . . . . . . . . . . 40\n\n2.1.2 Charge transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42\n\n2.1.3 The pn junction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43\n\n2.1.4 Physical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45\n\n2.2 Radiation detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52\n\n2.2.1 The energy deposition process . . . . . . . . . . . . . . . . . . . . . . 52\n\n11\n\n\n\nTABLE DES MATIÈRES\n\n2.2.2 Signal formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53\n\n2.3 The Hybrid Planar Pixel Sensor . . . . . . . . . . . . . . . . . . . . . . . . . 54\n\n2.4 Other Silicon sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56\n\n2.4.1 the 3D pixel sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58\n\n2.4.2 High Resistivity Monolithic Active Pixel Sensors (MAPS) . . . . . . 58\n\n2.5 Radiation damage in Silicon sensors . . . . . . . . . . . . . . . . . . . . . . 59\n\n2.5.1 Non-ionizing Energy Loss (NIEL) . . . . . . . . . . . . . . . . . . . . 59\n\n2.5.2 Ionizing energy loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66\n\n3 TCAD Simulation models 69\n\n3.1 Process simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71\n\n3.2 Device simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74\n\n3.2.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74\n\n3.2.2 boundary conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 75\n\n3.3 The Multi-Guard Ring structure . . . . . . . . . . . . . . . . . . . . . . . . 79\n\n3.3.1 Principles of guard ring structures . . . . . . . . . . . . . . . . . . . 81\n\n3.3.2 Optimization of guard ring structures for reduction of inactive area\n\nand radiation hardness . . . . . . . . . . . . . . . . . . . . . . . . . 84\n\n3.3.3 The Slim Edge Guard Ring structure . . . . . . . . . . . . . . . . . . 103\n\n3.4 The charge amplification mechanism in highly irradiated silicon sensors . . . 108\n\n4 From TCAD simulation to experimental data 117\n\n4.1 Experimental validation of TCAD simulation . . . . . . . . . . . . . . . . . 118\n\n4.1.1 Doping profile measurements . . . . . . . . . . . . . . . . . . . . . . 121\n\n4.1.2 Guard Ring measurements . . . . . . . . . . . . . . . . . . . . . . . . 136\n\n4.1.3 Current versus Bias characteristics . . . . . . . . . . . . . . . . . . . 141\n\n12\n\n\n\nTABLE DES MATIÈRES\n\n5 Planar Pixel Sensor digitization for ATLAS IBL simulation 149\n\n5.1 Test beam validation of TCAD simulation and digitization . . . . . . . . . . 152\n\n5.1.1 Validation of the digitization model . . . . . . . . . . . . . . . . . . . 153\n\n5.1.2 Edge effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157\n\nConclusion 163\n\nWord Cloud 170\n\nBibliography 170\n\n13\n\n\n\nTABLE DES MATIÈRES\n\n14\n\n\n\nIntroduction\n\nThe Large Hadron Collider, located in CERN, Geneva, has been delivering collisions\n\nof proton beam accelerated to an energy of 3.5 TeV since November 23 2009. since then,\n\nthe ATLAS experiment has been recording data to search for new physics to be discovered\n\nthrough analysis of the collision kinematic. The planned luminosity rise for the period\n\nbetween 2011 and 2020 will however bring new challenge to the detector that will need\n\nto be addressed to maintain the performance of the actual detector. The inner detector\n\nwill be the most challenged with the increased amount of tracks per collision to detect,\n\nresulting from the high luminosity upgrade. Radiation damage in its sensors and electronics\n\nwill eventually reduce the efficiency and lead to failure of the detector system. Aging of\n\nthe detector will also reduce the number of active channels and increase the detection\n\ninefficiency.\n\nAn intense R&D effort has been ongoing to design a new pixel detector capable of\n\nhandling increased occupancy linked to higher luminosity and able to resist to radiation\n\ndamage induced by the collisions. The Insertable B-Layer (IBL) project has been created to\n\nperform a first update of the detector by adding a new pixel layer between the beampipe\n\nand the actually inner layer of the pixel detector. The SLHC project plans to replace\n\nthe whole inner detector with an improved version able to withstand a ten fold increase\n\nin radiation damage and track multiplicity. In this work, I present a study, using TCAD\n\nsimulation, of the possible method of design of a planar pixel sensors reducing their inactive\n\narea and improving their radiation hardness. Different physical models available have been\n\nstudied to develop a coherent model of radiation damage in silicon that can be used to\n\npredict silicon pixel sensor behavior after exposure to radiation. The Multi-Guard Ring\n\nStructure used in pixel sensor design was studied to obtain guidelines for the reduction\n\n15\n\n\n\nINTRODUCTION\n\nof inactive edges detrimental to detector operation while keeping a good sensor behavior\n\nthrough its lifetime in the ATLAS detector. A campaign of measurement of the sensor’s\n\nprocess parameters and electrical behavior to validate and calibrate the TCAD simulation\n\nmodels and results will also be presented in this work. A model for charge collection in\n\nhighly irradiated diode was developed to explain the high charge collection observed in\n\nhighly irradiated devices.\n\nFinally , a simple planar pixel sensor digitization model to be used in test beam and\n\nfull detector system is presented. The simple model allow for easy comparison between\n\nexperimental data and prediction by the various radiation damage models available. The\n\ndigitizer has been validated using test beam data for unirradiated sensors and can be used\n\nto produce the first full scale simulation of the ATLAS detector with the IBL that include\n\nsensor effects such as slim edge and thinning of the sensor.\n\n16\n\n\n\nChapitre 1\n\nThe ATLAS experiment and upgrade\nproject\n\n1.1 The Large Hadron Collider\n\nThe Large Hadron Collider is a 27 km diameter proton accelerator and collider using\n\nsupra-conductive magnet technology, located in CERN, designed to operate at a nominal\n\nenergy of 7 TeV. The particle beam accelerated in the LHC originate from the accelerator\n\ncomplex present on site at CERN [1], as seen on figure 1.1. The proton synchrotron produce\n\na beam of an energy of 25 GeV that is the injected in the Super Proton synchrotron which\n\naccelerate the protons up to an energy of 450 GeV. The protons are then injected in the\n\nLHC and accelerated to an energy up to 7 TeV. The machine nominal operation luminosity\n\nis 1034 cm−2s−1. At the moment of writing these line, the LHC was operated at a beam\n\nenergy of 3.5 TeV for a peak luminosity of 8.3× 1032 cm−2s−1.\n\nFigure 1.2 show the peak luminosity recorded by the ATLAS detector and delivered by\n\nthe LHC for the period spanning from january 2011 to may 9th 2011. The luminosity is\n\nexponentially increasing toward the nominal value for which the machine was designed. A\n\nshutdown of the machine is planned for 2013 to allow maintenance and reparation. Work\n\non the magnet is scheduled to allow to reach nominal energy of 7 TeV per proton. Two\n\nbeam circulate in the clockwise and counterclockwise direction of the accelerator and can\n\ncollide at 4 main point around the ring, where are located the 4 main LHC experiments :\n\nATLAS, CMS, LHCb and ALICE.\n\n17\n\n\n\n1.1. THE LARGE HADRON COLLIDER\n\nFigure 1.1 – The LHC and CERN accelerator complex\n\nFigure 1.2 – ATLAS recorded Online peak luminosity per day for 2011\n\n18\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\n1.2 The ATLAS experiment\n\nThe ATLAS (A Toroidal LHC ApparatuS) experiment is a particle collider installed on\n\nthe LHC experiment. This general purpose detector focus on the detection of the elusive\n\nHiggs boson through various decay channels and on the research of new physics beyond\n\nthe standard model . The ATLAS detector is composed of three main concentric sub-\n\ndetector represented on figure 1.3 : The inner detector, used for trajectory and impulsion\n\nmeasurement of charged particle, the calorimeters measuring the electromagnetic and ha-\n\ndronic energy deposition of the particles emanating from the interaction point and the\n\nmuon spectrometer used to measure the impulsion and trajectory of the weakly interacting\n\nmuons. The inner detector is enclose in a solenoid with a magnetic field of 2 T allowing\n\nthe measurement of the transverse momentum of the particle crossing its volume using the\n\ncurvature of the track in the magnetic field oriented along the beam direction. The muon\n\nspectrometer is enclosed in a toroidal magnet system with a magnetic field of 4 T.\n\nFigure 1.3 – The ATLAS detector\n\nThe ATLAS detector is designed to operate at a collision frequency of 40 MHz with\n\n19\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nbunch crossing every 25 ns. The amount of interaction at this rate and luminosity would\n\nbe to large to be recorded and a on detector trigger system. called level 1 trigger, filter\n\nthe interesting event using the calorimetric information. The trigger system identify the\n\nbunch crossing timestamp and retrieve from the detector the information related to that\n\ntimestamp from the detector systems holding the data. Higher level trigger system are then\n\napplied to further reduce the amount of data and select only interesting event emanating\n\nfrom the collision of the proton in the center of the machine.\n\n1.2.1 The Inner detector\n\nThe inner detector [2] is an essential part of ATLAS that has for purpose to measure\n\nthe trajectories of particles produced during the collisions of high energy protons beam\n\nproduced by the LHC. The detector is composed of three subsystems : The pixel detector ,\n\nthe Semi-Conductor Tracker (SCT) and the Transition radiation Tracker (TRT). The pixel\n\ndetector consists of three concentric barrels of pixel detector modules located at 5.05, 8.85\n\nand 12.25 cm from the interaction point and of 6 disk, 3 on each side of interaction point,\n\nlocated at a mean z of 49.5, 58.0 and 65.0 cm with regard to the interaction point. Fig. 1.4\n\nshows the cylindrical layers formed by the Inner Detector’s subsystems. Particle beams will\n\ntravel in the cylinder’s Z direction and collision will take place at the geometrical center of\n\nthe inner detector cylindrical structure.\n\nThe barrels are assembled with 1744 modules, shown in figure 1.5, each containing 16\n\nfront-end electronics 2880 channel chips, labeled FE-I3, bump-bonded to the pixel sensors\n\nmounted on a flex-hybrid circuit board, along with a module control chip (MCC). The\n\nmodule are assembled into ladders then paired to form a bistave structure, shown in figure\n\n1.6a. The bistaves are then assembled on the support tube to form half shell assemblies ,\n\nshown in figure 1.6b, that are then clamped together to form a pixel layer. The disk contain\n\neach 48 modules for a total of 288 modules for both end caps. The inner detector covers a\n\nregion of ±2.5η in the pseudo-rapidity coordinate system where η = −ln(tan(θ/2)). Each\n\nsensor exhibit pixels of 400x50 µm leading to a hit position reconstruction resolution of 12\n\nµm in φ at normal incidence.\n\nThe modules of the bistave assembly of the pixel barrels are overlapping in the Φ\n\n20\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nFigure 1.4 – 3D view of ATLAS Inner Detector, including Silicon tracker (SCT) and\nTransition Radiation Tracker (TRT) [3]\n\nFigure 1.5 – Schematic view of a barrel pixel module [2]\n\n21\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\ndirection (angular coordinate in the cylindrical coordinate system), as shown on Fig. 1.7.\n\nThis overlap of detector modules is necessary to ensure an hermetic tracking coverage in Φ\n\nneeded to separate simultaneous tracks from high transverse energy particles produced by\n\nthe collisions at the interaction point. The edges of the pixel modules are not active area\n\nand need to be covered by the active area of the preceding and following modules along the\n\nhalf-shell assembly. This overlap of the sensors increase the amount of material between\n\nthe interaction point and the calorimeters and has been reduced as much as possible to\n\nlimit its effect on data quality.\n\n(a) The bistave assembly (b) Half shell assembly\n\nFigure 1.6 – Bistave and half-shell structure of the ATLAS pixel detector [4]\n\nThe position of the three detector layers close to the interaction point lead to a high\n\nnumber of charged and neutral particles crossing the detector. Radiation damage in these\n\nzone near the origin of the interactions will affect the performance of the innermost detector\n\nsystem. The inner detector innermost pixel layer, the b-layer, will be exposed to fluences of\n\nthe order of 5×1015 neq/cm\n2) at the end of its lifetime. Figure 1.8 show the simulated level\n\nof radiation damage in equivalent to 1 MeV neutrons for the inner detector for a 107 seconds\n\nof operation at nominal luminosity. The pixel sensors forming the tracker will suffer from\n\nhigh fluences phenomena like the formation of a double Junction/double electric field peak\n\nand Space-Charge Sign Inversion (SCSI) [5; 6]. Radiation effects also include an increase\n\nin the bias potential required to fully deplete the pixel sensors as the acceptor-like traps\n\n22\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nFigure 1.7 – Layout of the barrel pixel modules, R-Phi plane view [3]\n\nconcentration in silicon increases with exposure to radiation. The sensors will be operatedat\n\nfull depletion voltage in order to measure significant charge signals from particles crossing\n\nthe sensors, as collected charge is reduced by the trapping in defects created by radiation.\n\nThe Semiconductor tracker, or SCT, shown in figure 1.9, surround the pixel detector.\n\nThe detector is composed of two layers of micro-strip silicon p-in-n sensors rotated by an\n\nangle of 40 mrad with regard to each other. Strip are 80 µm x 128 mm long with the 80\n\nµm pitch oriented in the φ direction. Four cylindrical layers are positioned at respective\n\nradius of 29.9, 37.1, 44.3 and 51.4 cm with regard to the interaction point at φ = 0. Nine\n\nend-caps complete the system. The end-caps are located on each side of the interaction\n\npoint at a distances between 85 and 272 cm of the interaction point in the z direction. The\n\nz and r positioning resolution of the SCT modules are respectively of 580 µm and 17 µm\n\nand cover a region of ±2.5η in pseudo-rapidity.\n\nThe final part of the inner detector is the Transition Radiation Tracker sitting at a\n\nradius between 55.4 and 108.2 cm of the interaction point. The system is composed of\n\n351000 tubes of 4mm diameter filled with a gas mixture of Xe, CO2 and O2. A gold plated\n\nwire travel through the middle of the straw to form a detection electrode. A bias voltage\n\nof 1530V is applied between the outer surface and the wire to form an electric field inside\n\n23\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nFigure 1.8 – Annual 1 MeV neutron equivalent fluences assuming 107 s at nominal lumi-\nnosity [7]\n\n24\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nFigure 1.9 – The inner detector layer structure\n\nthe tube where ions and electron can drift . Particle crossing the tube deposit energy by\n\nionizing the gas and creating free charge that drift toward the electrode. Electrons crossing\n\nthe tubes will also produce transition radiation photon that will yield additional signal\n\nwith regard to other charge particles, allowing identification of the electrons using a two\n\nthreshold system. Positioning resolution in the r-φ plane is 130 µm.\n\n1.2.2 The calorimeter\n\nThe calorimeter system, shown in figure 1.10 surrounds the inner detector and is used\n\nto accurately measure the energy of the particle and jets coming from collision interactions.\n\nThe calorimeter is divided into two main parts : The electromagnetic calorimeter and the\n\nhadronic calorimeter. The design of the system has been done to minimize the dead area\n\nin the coverage in the r-φ plane and maximize the uniformity of the response to energy\n\ndeposition.\n\nThe electromagnetic calorimeter is a sampling calorimeter using liquid argon as the\n\n25\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\nFigure 1.10 – The ATLAS calorimeter system\n\nFigure 1.11 – The ATLAS calorimeter system\n\n26\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\ndetection medium. The accordeon electrode geometry, shown in figure 1.11 allow to obtain a\n\ncomplete r-φ coverage and uniform material budget. The barrel part of the electromagnetic\n\ncalorimeter extend between ±3.2η while the endcaps cover the range of 3.2 < |η| < 4.9.\n\nThe barrel of the calorimeter is divided in three longitudinal section for |η| < 2.5. and two\n\nsegment for 2.5 < |η| < 3.2. A presampler calorimeter with fine granularity strips is present\n\nfor |η| < 1.8 to allow correction for energy loss in the inner detector , cryostats and service\n\nlocated before the calorimeter. The measured energy resolution of the electromagnetic\n\ncalorimeter has been found to be described by equation 1.1 where a is a stochastic term\n\nequal to 10% and b a constant equal to 0.17%.\n\nσ(E)\n\nE\n=\n\na√\n(E(GeV ))\n\n⊕ b (1.1)\n\nThe forward region of the calorimeter, located in 3.1 < |η| < 4.9, is called the For-\n\nward Calorimeter (FCAL) and also use liquid argon as a detection medium. The readout\n\nelectrode are cylinder parallel to the beam direction inserted in a copper matrix with a\n\ndistance of 250 µm between the electrodes and the wall of the matrix, as shown in figure\n\n1.12. A quartz spacer is used to maintain the distance between the electrode tube and the\n\ncopper matrix and liquid argon fills the gap between the two surfaces.\n\nThe hadronic calorimeter is divided into two subsystems : The Tile Calorimeter and\n\nthe Hadronic End-Cap plus Forward Calorimeter. The Tile Calorimeters extend for the\n\n|η| < 1.6 pseudorapidity region of the detector. It is composed of modules scintillating tiles\n\nstacked between Iron absorbers slabs, as shown on figure 1.13, . Each tile is read through\n\nan wavelength shifting optical fibers coupled to photomultipliers. Timing resolution of the\n\nphotomultipliers is 23.5 ns , allowing to tag hadronic interaction to the correct Level 1\n\ntrigger clock.\n\nThe Forward Calorimeters and Hadronic End-Cap are liquid Argon sampling calorime-\n\nters located at each end of the detector, in the 3.1 < |η| < 4.9 pseudo-rapidity region. The\n\nHadronic End-Cap is formed of 128 modules of parallel plate liquid argon chambers , 32\n\nfor each wheel, an example of which can be seen in figure 1.14. The FCAL use the same\n\nelectrode structure as the electromagnetic calorimeter with a Tungsten matrix instead of\n\n27\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\n(a) FCAL endcap copper matrix (b) FCAL endcap electrode geometry\n\nFigure 1.12 – The ATLAS forward calorimeter electrode structure and its copper matrix\n\nFigure 1.13 – The ATLAS Tile Calorimeter module\n\n28\n\n\n\n1.2. THE ATLAS EXPERIMENT\n\ncopper. Disposition of the different forward calorimeter system is described in figure 1.15.\n\nFigure 1.14 – An ATLAS End-Cap calorimeter wheel\n\nFigure 1.15 – ATLAS Forward region disposition of calorimeters systems\n\n1.2.3 The muon spectrometer\n\nThe muon spectrometer, shown in figure 1.16, is of a rapid detection system located\n\noutside the toroidal magnet system, used for triggering on muons coming from interactions,\n\nand a precision measurement system located inside the toroid magnetic field and used to\n\nmeasure accurately the impulsion of the muons by measuring the curvature of their tracks\n\nin the field. Three type of detection module are used in the detector system :\n\nThe Thin-Gap chamber (TGC)\n\n29\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nThe Cathode strip chambers (CSC)\n\nThe Monitored drift tubes (MDT)\n\nThe Resistive-plate chambers (RPC)\n\nThe precision measurement chambers are located in the central region of the detector\n\nat a radius of 5, 7.5 and 10 m with regard to the interaction point, and in the End-Cap\n\nregions at z = 7 and 22 m. They are composed of MDT and CSC modules. the fast trigger\n\nchambers are present in the |η| pseudo-rapidity region. They are composed of RPC for the\n\nbarrel section and TGC for the end caps.\n\nFigure 1.16 – The ATLAS Muon Spectrometer\n\nThe ATLAS detector has been recording data since november 23 2009. The readiness of\n\nall the subsystem has allowed a rapid progress in energy, luminosity and physics analysis.\n\nFigure 1.17 show an example of a Z muon candidate in the 2010 run data decaying into\n\n2 back to back electrons with transverse energy of 40 and 45 GeV. The electron track\n\nreconstruction in the inner detector is shown, along with their measured energy in the\n\nelectromagnetic calorimeter.\n\n1.3 THe ATLAS upgrade projects\n\nThe LHC was designed to produce a luminosity of 1034cm−2s−1. An upgrade of the\n\naccelerator is planned to increase even further the luminosity delivered by the machine.\n\nThree phase have been planned in the upgrade project [8]. Phase 1 will consist of an\n\n30\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nFigure 1.17 – Z boson candidate in the ATLAS detector\n\nincrease to a luminosity up to 2.3×1034cm−2s−1 without any modifications to the machine\n\nitself by pushing the machine to its maximum performance. Phase 2, called the SLHC\n\nphase, would see a factor 10 increase in luminosity with regard to the nominal luminosity.\n\nModification to the injection system will be performed to increase injection energy in the\n\nLHC at 1TeV, instead of the 450 GeV currently produced by the SPS. Modification to\n\nthe insertion quadrupole and to the machine parameters would then allow to increase\n\nluminosity.\n\nThe ATLAS pixel detector, located very close to interaction point, will see a increasing\n\namount of radiation as a consequence of the luminosity upgrade. The system was designed\n\nto operate at the actual nominal luminosity and modification to the system will be needed\n\nto cope with the increased pile-up of interaction at each bunch crossing to ensure a stable\n\ndetection efficiency, independent of luminosity. Two upgrade projects are planned to add\n\na new pixel layer and eventually replace the entire pixel detector to handle the problems\n\nbrought by an increase in luminosity : The Insertable B-Layer (IBL) and the SLHC upgrade.\n\n31\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\n1.3.1 Phase 1 : The Insertable B-Layer (IBL)\n\nThe IBL project [9] consist in building a new layer for the pixel detector that will be\n\ninserted between the beampipe and the actual b-layer. The main motivation behind this\n\ndetector upgrade are :\n\n1. Tracking Robustness : Failure of actual module as they age and suffer from radiation\n\ndamage will affect tracking efficiency and precision. The addition of an additional\n\nspace point will provide a more robust tracking by compensating for the failed sensors\n\nand by giving information about tracks from a distance closer to interaction point.\n\n2. Luminosity effects : Increase in luminosity will produce more tracks per bunch cros-\n\nsing to be reconstructed by the pixel detector. Readout inefficiencies on the current\n\nsensor will reduce the overall detection inefficiency and the IBL will provide a system\n\nwith a increased occupancy capacity to compensate for this effect.\n\n3. Tracking precision : The location of the IBL, closer to interaction point, will allow\n\nfor more accurate reconstruction of the primary vertex and b-tagging. The insertion\n\nof the IBL will result in an increase in sensitivity to physics channel using these\n\nparameters.\n\n4. Radiation Damage : The actual b-layer is foreseen to resist to radiation doses of 1×\n\n1015neqcm\n−2. In the best scenario of luminosity upgrade, radiation damage inflicted\n\nduring pixel detector operation could eventually lead to a failure of the system due\n\nto radiation damage. The insertion of the IBL will provide a insurance policy against\n\nsuch problems if they were to occur.\n\nThe main parameters of the IBL are described in table 1.1. Two sensor candidate are\n\ncurrently considered to build the IBL : Planar Pixel Sensor and 3D Silicon Sensors. The\n\ntwo technology require different module design, with a single chip module being used for\n\n3D sensors while a double chip module is to be used with Planar sensors. A new readout\n\nchip has been developed to replace the FE-I3 used in the current ATLAS pixel modules.\n\nTable 1.2 show a comparison of the main characteristics of the two readout chip. The\n\nFEI4 can be distinguished by it larger size, smaller pixels, increased radiation hardness\n\nand occupancy performance. The timing resolution is kept at the same level as before but\n\n32\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nthe energy deposition measurement resolution is reduced from 8 to 4 bits. The reduction\n\nof the resolution was done to allow higher occupancy in the readout chip.\n\nTable 1.1 – Main IBL layout parameters [9]\n\nParameter Value\nNumber of staves 14\n\nNumber of modules per stave (single/double FEI4) 32/16\nPixel size (φ,z) 50,250 µm\n\nModule active size WxL (single/double FEI4 modules) 16.8 x 40.8/20.4 mm2\n\nCoverage in η |η| < 3.0\n\nOverlap in φ between staves 1.82 degree\nCenter of sensor radius 33.25 mm\n\nSensor thickness 150-250 µm\nRadiation length at z=0 1.54% of X0\n\nTable 1.2 – Comparison of FE-I4 and FE-I3 readout chip\n\nParameter FE-I4 FE-I3\nTechnology IBM 130nm IBM 250nm\n\nPixel size (µm) 250x50 400x50\nNr. of Channel 26880 2880\n\nMatrix dimension 80 column sx 336 rows 18 columns x 164 rows\nhline Time-Over-Threshold resolution 4 bits 8 bits\n\nTiming resolution 25 ns 25 ns\nHit Buffer 32 per 4 pixels 64 per column pair\n\nThe IBL will be positioned at 3.325 cm of the interaction point and it will be required\n\nto replace the actual beampipe , located at a radius of 2,9 cm, by a new beampipe with a\n\nradius of 2,5 cm to liberate space to insert the IBL in the ATLAS pixel detector. Figure\n\nThe planar pixel sensor candidate for IBL was developed within the Planar Pixel Up-\n\ngrade Group. Three model were developed, using recommendation from the simulation\n\nwork presented in this thesis :\n\n– Conservative n-in-n : ATLAS Standard sensor with a reduction of the guard ring by\n\nremoval of the 3 outer guard ring to obtain the edge width of 450 µm required for\n\nIBL\n\n33\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nFigure 1.18 – IBL r − φ layout view\n\n(a) IBL material budget (b) FInner detector material budget (IBL included)\n\nFigure 1.19 – Radiation length as a function of η in the IBL and inner detector\n\n34\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\n– Slim Edge n-in-n ATLAS sensor : ATLAS Standard sensor with 3 guard ring removed\n\nand guard ring shifted under the pixels to obtain an inactive edge of 100 µm.\n\n– n-in-p sensor : Planar pixel sensor using a p substrate and a 450 µm reduced guard\n\nring structure\n\nFigure 1.20 shows the edge geometry for the three candidate. More details on the\n\nstructure, their behavior and design will be given in the next chapters.\n\n(a) Conservative n-in-n sensor (b) Slim edge n-in-n sensor (c) N-in-p sensor\n\nFigure 1.20 – Planar pixel sensor candidates for IBL\n\n1.3.2 Phase 2 : Upgrade for high luminosity\n\nThe phase two of LHC upgrade will require a completely new inner detector to survive\n\nluminosity and pile-up foreseen in LHC. The amount of radiation damage on the pixel\n\nsensors will increase by a factor 10 with regard to the current pixel detector which will not\n\nbe able to cope with such occupancy and radiation damage. Research and development is\n\nnow ongoing to design a new layout for the ATLAS super-LHC inner detector. Figure 1.21\n\nshows the current accepted layout planned for the upgrade. The TRT is removed along\n\nwith the whole actual inner detector and replaced by 4 layers of pixels, 3 layers of short\n\nstrips and 2 layers of long strips [10].\n\nPlanar pixel sensor could be used for the SLHC pixel detector if it can yield enough\n\nsignal at the 1× 1016neqcm\n−2 fluence expected for the inner layer of the pixel detector. In\n\nchapter 3, a study of a new physical phenomenon observed in highly irradiated conditions\n\nhas been performed to understand the physics mechanism behind the formation of collected\n\nsignal in highly irradiated silicon sensors. The charge amplification can be explained by\n\nimpact ionization current and trap-to-band tunneling in the bulk of the sensor and could\n\n35\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nFigure 1.21 – ATLAS super-LHC inner detector planned z − yi layout view\n\n36\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\nbe used to design a new sensor able to survive the harsh environment of the SLHC ATLAS\n\ndetector. The R&D done in the framework of the IBL upgrade also covers the needs of the\n\nSLHC detector and results presented in this work can also be applied to detectors in the\n\nSLHC environment,\n\n37\n\n\n\n1.3. THE ATLAS UPGRADE PROJECTS\n\n38\n\n\n\nChapitre 2\n\nPrinciples of Silicon pixel sensors\n\nSilicon-based detector have been used for the last 60 years as an efficient mean to\n\ndetect the presence of charged particles. Gold contact barrier, then p-n junction diodes\n\nwere used between 1955 and 1965 as an efficient small size spectroscopic sensor to measure\n\nthe ionizing energy deposition of β particles in silicon. The first HEP experiment to make\n\na wide use of silicon as a tracking detector was CERN’s NA11 and NA32 experiments [11].\n\nThe strip sensors used in their tracking system shown the possibility of large scale usage\n\nof these sensors in tracking applications in HEP.\n\nNowadays, the wide use of silicon diode based sensors allowed for the development of\n\nvery sophisticated detectors used in large scale experiments where high resolution tracking\n\nof charged particles is required. Their increased exposure to radiation in high luminosity\n\nexperiments due to their proximity to the interaction points remains a challenge as aging\n\neffects have been observed with increasing exposure to high energy particles. In this chapter\n\nI will describe the basic semiconductor physics behind the p-n junction based detectors and\n\nthe mechanism that allow them to be used as efficient radiation detection devices. I will\n\ndescribe the ATLAS Hybrid planar pixel sensor which is the main study subject of this\n\nthesis. A quick survey of the concurrent technologies will be presented.\n\nFinally, the physics behind the aging effects of silicon detectors due to exposure to\n\nradiation will be described. The empirical models developed in the recent years will be\n\ncompared to the microscopic effects of radiation damage of silicon to provide an explanation\n\nof the behavior observed in irradiated silicon sensors.\n\n39\n\n\n\n2.1. THE PHYSICS OF SILICON\n\n2.1 The physics of Silicon\n\n2.1.1 Semiconductors properties\n\nSilicon used in particle detection is a semiconductor with a face-centered cubic crystal-\n\nline structure, as shown in figure 2.1.\n\nFigure 2.1 – Unit cell of silicon crystalline structure\n\nThe periodic nature of the silicon crystal lattice create the conditions for the electrons\n\nof the silicon atoms to arrange, in the energy-impulsion domain, in a band structure with\n\nforbidden zones where an electron cannot be found. This energy region where electrons\n\ncannot be found is called the bandgap. The height of this region is a unique property of\n\neach semiconductor.\n\nThe valence electrons of the silicons atoms are distributed between the conduction band\n\nand the valence band. The electrons in the conduction band are weakly bound to the lattice\n\nand can freely move through the material while the valence electrons are constrained to\n\nstay close the lattice structure. The Pauli exclusion principle forbids that two fermions,\n\nsuch as electrons, lie in the same energy-impulsion state. The energy distribution of this\n\ngas of fermions can be best described by the Fermi-Dirac distribution function (eq. 2.1) :\n\nni =\ngi\n\ne\nEi−µ\nkBT + 1\n\n(2.1)\n\nwhere gi is the degeneracy factor of the energy state and µ is the Fermi-Dirac quasi-energy\n\n40\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nlevel. At low temperature (kBT << µ), µ is the energy level under which all state are\n\noccupied. At high temperature (kBT >> µ),µ is the energy level over which a state has\n\nonly a probability of 50% of being occupied. The Fermi-Dirac distribution at different\n\ntemperature is shown in figure 2.2.\n\nFigure 2.2 – Fermi-Dirac Distribution, µ = 0, gi = 1\n\nThis principle leads to the introduction of a virtual particle, the hole, as the carrier\n\nof the \"absence of an electron\" in the valence band of silicon. The addition of impurities\n\nto the crystalline structure modify the macroscopic properties of silicon. When impurities\n\ninserted in the crystal structure have a number of valence electrons (Nve) different than\n\n4, for silicon, extra electrons (Nve > 4) or holes (Nve < 4) are introduced in the band\n\nstructure. The hole introducing dopant is called an acceptor and is found in p type silicon.\n\nThe electron introducing impurities are called donors and are found in n type silicon.\n\nBoth type of dopant can be present in silicon and will compensate each other in such\n\nway that only the net dopant concentration will be electrically active. In n type and p\n\ntype semiconductors , the Fermi-Dirac quasi-energy level is found respectively close to the\n\nconduction or the valence band. The additional carriers will fill energy states not occupied\n\nin the intrinsic silicon modifying the position of the Fermi-Dirac quasi-energy level. At high\n\n41\n\n\n\n2.1. THE PHYSICS OF SILICON\n\ntemperature typical of the operation of silicon sensors, most of the impurities additional\n\ncarriers are added to the cloud of free carriers in the silicon crystalline structure, increasing\n\nthe amount of carriers available for conduction. In highly doped silicon (ND,A > 1018cm−3),\n\nthe amount of additional carriers is such that energy levels in the valence band, for holes,\n\nor in the conduction band, for electrons, have a probability close or equal to 1 of being\n\noccupied. This lead to high conductivity of the material even at low temperature.\n\n2.1.2 Charge transport\n\nThe dynamics of the carriers inside silicon can be described by the drift-diffusion equa-\n\ntions (eq. 2.2 and 2.3) coupled to the Poisson (eq. 2.4) [12] :\n\ndp\n\ndt\n= ∇ ·Dh∇p+∇ · (pµh ~E) +Gh −Rh (2.2)\n\ndn\n\ndt\n= ∇ ·De∇n−∇ · (nµe ~E) +Ge −Re (2.3)\n\n−∇2V = ∇ · ~E =\nρ\n\nε\n(2.4)\n\nwhere p and n are respectively the density of holes and electrons in [ 1\ncm3 ], D in [ cm\n\n2\n\ns ],\n\ntheir respective diffusion coefficient, µ the mobility in [cm2/V/s]. G is the generation rate\n\nand R, the recombination rate, both in [1/cm3/s]. The h and e subscript respectively design\n\nholes and electrons. ρ is the net charge density in [C/cm3], where C are Coulomb.\n\nGeneration/Recombination terms are important to describe the behavior of silicon de-\n\ntectors. Generation is responsible for leakage current present in detectors under bias. Re-\n\ncombination occurs between free carriers and its rate is proportional to the concentration\n\nof the most rare carrier. Silicon being an indirect gap semiconductor, generation and re-\n\ncombination occurs mostly through the defect states that are present in the bandgap of\n\nSilicon.\n\n42\n\n\n\n2.1. THE PHYSICS OF SILICON\n\n2.1.3 The pn junction\n\nWhen two regions of silicon containing different concentration of free carriers are put\n\ninto contact, we form a junction if the two region are respectively of p and n type. At the\n\ncontact region, the excess holes and electrons present create an electric field dragging free\n\ncarriers on the other side of the junction . A space-charge region is created at the junction\n\nwhere the electric field is present. The rate of carriers entering the space-charge region\n\nby diffusion from the doped region is equal to the rate of carriers leaving this region by\n\ndrifting in the electrical field built by the difference of carrier concentration on each side\n\nof the junction. A zone with very low concentration of free carriers that is created at the\n\njunction is called the depletion zone. The only free carriers present are coming from the\n\nthermally generated carriers created to replace the carriers drifting away in the electric\n\nfield. The application of an additional electric field through electrodes in contact with each\n\nside of the junction will modify this equilibrium by dragging carriers from the doped regions\n\naway or within the junction and modify the width of the depletion zone.\n\nThe pn junction can be biased in two different manners, as illustrated in figure 2.3.\n\nIf a negative voltage is applied on the n side and a positive voltage on the p side of the\n\njunction, the depletion zone tends to shrink and the electric field in the depletion zone is\n\nreduced, allowing more charge to cross the potential barrier and diffuse in the opposite side\n\nof the junction. This results in a reduction of the apparent resistance of the pn junction\n\nand a non-linear exponentially increasing current. In the opposite case , the depletion\n\nregion will expand away from the junction and the electric field increases as the amount\n\nof space charge becomes more important. The only current flowing through the junction is\n\nthe diffusion current from each depletion region edges, which saturates with distance, and\n\nthe generation/recombination current from the depletion region.This result in an apparent\n\nincrease of the resistance of the pn junction and results in a saturation of leakage current.\n\nFigure 4.21 show the typical Current versus bias of a pn junction.\n\nThe depleted zone in reverse bias mode represent the volume where particle energy\n\ndeposition can be detected as carriers generated through this process can drift into the\n\nelectric field and generate a signal. The width of the depletion zone can be calculated using\n\n43\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nFigure 2.3 – The three possible state of a pn junction : at rest (top), forward bias (middle),\nreverse bias (bottom). The depletion region relative size is shown on the figure with the\nspace-charge sign of each zone of the diode.\n\nequation 2.5 [13].\n\nd =\n\n√\n2εV\n\neN\n(2.5)\n\nWhere ε is the electrical permittivity in silicon, V the applied bias voltage, e the electron\n\ncharge and N the dopant concentration in the region where dopants are less concentrated.\n\nDopant concentration can be linked to sensor resistivity using equation 2.6, details of the\n\ncalculation are given in the annex.\n\nρ =\n1\n\neND,Aµn, p\n(2.6)\n\n44\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nFigure 2.4 – Typical pn junction current versus bias characteristics.\n\nWhere ND,A is the donor or acceptor effective concentration.\n\n2.1.4 Physical models\n\nIn order to simulate correctly the behavior of silicon detectors, the generation and\n\nrecombination terms of the drift-diffusion equations, mobilities of the carriers, oxide-silicon\n\ninterface and metal silicon interface physics must be correctly modeled. The dependence of\n\nmodels on electric field magnitude, temperature, dopant concentration must be taken into\n\naccount correctly to obtain an accurate and quantitative simulation of a device.\n\n2.1.4.1 Generation-Recombination\n\nThe rates of generation and recombination of thermally generated carriers are descri-\n\nbed by the modified Shockley-Read-Hall equation ([12]), which describes the generation-\n\nrecombination in indirect-band gap semiconductors such as silicon. This model assumes\n\nthat the transition of carriers between bands occurs through a single trap energy level\n\n45\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nlocated deeply in the gap, Etrap.\n\nRSRH =\npn− n2\n\ni\n\nτp[n+ nie\nEtrap\nkbT ]τn[p+ nie\n\n−Etrap\nkbT ]\n\n(2.7)\n\nτn =\nτn0\n\n1 +\nNdopant\nNSRHn\n\n(2.8)\n\nτp =\nτp0\n\n1 +\nNdopant\nNSRHp\n\n(2.9)\n\nEquation (2.7) gives the Concentration-Dependent Shockley-Read-Hall Generation-\n\nRecombination model used in our simulation, where (2.8) and (2.9) explicit the concen-\n\ntration dependence. τp,n are the recombination lifetime for holes and electrons, τn0, τp0 =\n\n10−5 s a material dependent parameter representing the recombination lifetime for low\n\ndopant concentration bulk, NSRHp,n = 5× 1016 cm−3 a material dependent empirical pa-\n\nrameters and Ndopant the dopant concentration. The user dependent parameters must be\n\nchosen to represent the bulk material simulated. The values presented here were selected\n\nto represent a typical high resistivity bulk used for particle detectors that are not pure\n\ncrystals. The presence of oxygen and other impurities affects its electrical properties. Do-\n\npant are also introduced during fabrication of the sensors whereas defects are introduced\n\nby high energy particles crossing the sensor. In the super-LHC environment , ATLAS inner\n\ntracker will be exposed to high level of radiation and the large introduction of structu-\n\nral defects must be taken into account in the design of the sensors. More sophisticated\n\nsimulations of bulk properties like leakage current requires a more complex description of\n\ngeneration-recombination mechanisms.\n\nOur simulation of irradiated sensors use the modified Shockley-Read-Hall Generation-\n\nRecombination model, which can take into account the presence of multiple trap levels in\n\nthe band gap, introduced by radiation or native defects. Generation-Recombination terms\n\nfor each trap are calculated using (2.7) and a global term Rtotal is calculated following\n\n46\n\n\n\n2.1. THE PHYSICS OF SILICON\n\n(2.10).\n\nRtotal =\nl∑\n\nα=1\n\nRDα +\nm∑\nβ=1\n\nRAβ (2.10)\n\nτn,p =\n1\n\nNtvth,n,pσn,p\n(2.11)\n\nl and m are the numbers of donors and acceptors traps,RA,β RD,α the Generation-\n\nRecombination terms for respectively acceptors and donors traps. The density of traps Nt\n\nis taken into account through the parameters τn and τp used for each trap level, as shown\n\nin (2.11) .\n\nFinally, charge states of traps are taken into account in Poisson equation right term.\n\nThe amount of ionized trap is determined using Boltzmann statistics. This complex model\n\ntake into account the variation of the effective doping density and temperature depen-\n\ndence and model correctly the contribution of thermally generated carriers of generation-\n\nrecombination term of the transport equations. However, the presence of an intense electric\n\nfield (O(100kV cm−1)) alter the bandgap structure of silicon and enhance the generation\n\nand recombination of carriers.\n\nThe electric field presence causes a bending of the bandgap structure in space, lowering\n\nthe potential barrier faced by carriers to cross to or from the traps present in the bulk\n\nmaterial, as shown in figure 2.5.\n\nThis affects the lifetime of electrons and holes trapped in the defects in the band gap\n\nof silicon. An increased electric field in the bulk of the sensor will bend the band gap of\n\nsilicon modifying the energy level of the conduction and valence band between different\n\nspace points. If this bending is sufficient, tunneling of the carriers trapped in the defects\n\nto the valence or conduction band can occur, reducing the effective lifetime of the trapped\n\ncarrier and contributing to the leakage current and thus to the generation rate term of\n\nthe drift-diffusion equations. This physical phenomenon is called trap-to-band phonon-\n\nassisted tunneling. A large amount of defects are present in irradiated silicon and high\n\nvoltage operation is needed to obtain charge collection recovery. A model elaborated and\n\nintroduced in our simulation is described in details in [14]. In this model, trap lifetime of\n\n47\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nFigure 2.5 – Schematics of bandgap bending due to electric field and how it enhance the\ntunneling between traps and the bands.\n\nthe generation recombination terms of equation 2.8 and 2.9 is modified following equation\n\n2.12\n\nτp,n =\nτ0\np,n\n\n1 + ΓDiracp,n\n\n(2.12)\n\nΓDiracp,n =\n∆Ep,n\nkbT\n\n∫ 1\n\n0\ne\n\n(\n∆Ep,n\nkbT\n\n−Kp,nu3/2)\ndu (2.13)\n\nKp,n =\n4\n\n3\n\n√\n2m0m∗∆E3\n\np,n\n\n2q~E\n(2.14)\n\nWhere τ0\np,n is the trap lifetime without electric field, ∆Ep,n, the trap to band energy dif-\n\nference, m0 the effective carrier mass, m∗ = 0.15 the effective tunneling mass of the carrier,\n\nq the elementary charge, ~ the planck constant and E the local electric field magnitude.\n\nFigure 2.6 shows the typical trapped charge lifetime dependence on electric field fol-\n\nlowing the model introduced to the simulation. As the time scale of the charge drift in\n\nsilicon sensors is O(10 ns) this effect can be important for the charge collection in irra-\n\ndiated sensors operated at high bias voltage. The effect of trap-to-band tunneling is taken\n\ninto account in transient simulation by affecting the terms of equation 2.29\n\n48\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nFigure 2.6 – Trapped charge lifetime dependence on electric field, Etrap = Ec − 0.53 eV\n\nThe presence of an intense electric field in the bulk of silicon will lead to acceleration\n\nof carriers and generation of additional electron hole pairs by energy transfer from the\n\naccelerated carrier to the lattice electrons by coulombian scattering. This effects lead to\n\nbreakdown of silicon sensors and must be modeled to reproduce this behavior in simulation.\n\nA model of impact ionization has also been implemented in the generation rate of the drift-\n\ndiffusion equation. This model is the Selberherr Impact ionization model [15]. Equation\n\n(2.15) shows the relation used to obtain the generation rate contribution from impact\n\nionization, with the electric field dependence detailed in equation (2.16).\n\nGimpact = αn( ~E)\n∣∣∣ ~Jn∣∣∣+ αp( ~E)\n\n∣∣∣ ~Jp∣∣∣ (2.15)\n\nαn,p( ~E) = An,pe\n−Bn,p\n\n~E (2.16)\n\nCoefficients An,p and Bn,p are determined experimentally and are chosen as a function of\n\nthe material. Fig. 2.7 shows the electric field dependence of the impact ionization coefficient\n\nαn. If the electric field inside the irradiated sensor bulk reaches a magnitude of the order\n\nof 100 kV cm−1, some multiplication effects leading to increased leakage current are to be\n\nexpected.\n\n49\n\n\n\n2.1. THE PHYSICS OF SILICON\n\nFigure 2.7 – Impact generation coefficient αn dependence on electric field magnitude\n\n2.1.4.2 Mobility\n\nMobility of charge carriers in silicon is influenced by the magnitude of the parallel elec-\n\ntric field in which it is drifting. In a high electric field, free carrier’s energy loss by inelastic\n\nscattering in the crystal lattice will balance with the energy gained from acceleration in\n\nthe electric field. This leads to the saturation of the carrier’s speed [12]. This effect can be\n\nexpressed in terms of a variation of the mobility (µ) as a function of the parallel electric\n\nfield magnitude [16]. Equation 2.17 and 2.18 show the typical expression used to model the\n\nparallel field dependence of mobility in Silicon.\n\nµ(E) = µ0(\n1\n\n1 + (µ0E\nvsat\n\n)β\n)−β (2.17)\n\nvsat =\nα\n\n1 + θe\nTL\n\nTnominal\n\n(2.18)\n\nWhere TL is the lattice temperature. Typical values used for this model are shown in\n\n50\n\n\n\n2.1. THE PHYSICS OF SILICON\n\ntable 2.1.\n\nTable 2.1 – Parallel field dependence mobility model parameters\n\nParameter Electrons holes\nµ0 (cm2/V s at 300K) 1400 450\n\nβ 2.0 1.0\nα (cm/s) 2.4× 10−7 2.4× 10−7\n\nθ 0.8 0.8\nTnominal (K) 600 600\n\n2.1.4.3 Magnetic field effects\n\nSemiconductors sensors for particle tracking application are often used in a magnetic\n\nfield to allow the measurement of the transverse momentum of incoming particles. While\n\nthe presence of a field modify the behavior of the detectors, they can be operated in intense\n\nmagnetic field without hurting the detector performances. The carriers inside the magnetic\n\nfield are subject to the Lorentz force (equation 2.19), where is the velocity of the carrier\n\nand B the magnetic field intensity.\n\n~F = q( ~E + v × ~B) (2.19)\n\nThe additional component of the force due to the magnetic field presence leads to\n\ncarriers drifting away from the electric field lines with an angle determine by equation\n\n2.20 [17], where theta is the angle between the electric field and the actual carrier drift\n\ntrajectory and µHp,n is the Hall mobility, which differs from normal mobility because of the\n\neffects of the presence of the magnetic field.\n\ntanθp,n = µHp,nB (2.20)\n\n51\n\n\n\n2.2. RADIATION DETECTION\n\n2.2 Radiation detection\n\n2.2.1 The energy deposition process\n\nThe reverse bias operation mode of the silicon pn diode presents interesting characteris-\n\ntics for charged particle and x-ray detection. The radiation interacting with silicon diodes\n\nthrough ionizing process such as photoelectric effect, Compton scattering and through\n\ntransfer of energy to the bound carriers by coulombial scattering generate an amount of\n\nfree carriers in electron-hole pairs, proportional to the amount of energy deposited in the\n\ndiode through these ionizing processes. The mean amount of carriers generated in such\n\nprocess (Ne,h) is given by eq. 2.21 where Ed is the deposited energy and Eg is the pair\n\nproduction energy, 3.64eV for silicon.\n\nNe,h =\nEd\nEg\n\n(2.21)\n\nThe pair production process is an almost stochastic one, but the pair production process\n\nis not independent from pair to pair as the energy deposition occurs in a cascade of energy\n\ntransfers from the incoming particle to the carriers and from excited carriers to other\n\ncarriers. This correlation between the different pair production events gives rise to the\n\nFano factor (F=0.118 for silicon [18])in the intrinsic energy resolution equation (eq. 2.22)\n\nof semiconductor sensors to account for the lower standard deviation observed with regard\n\nto the Poisson predicted standard deviation.\n\nσSi =\n√\nFNe,h (2.22)\n\nIn a silicon diode used as a particle sensor, a diode is biased and the depleted re-\n\ngion represents the active detection region. A particle deposit its energy and a cloud of\n\nelectron-hole pairs are created. Following the electric field, they drift toward the electrodes\n\ngenerating an additional current in the diode that can be measured using detection elec-\n\ntronics, as shown in figure 2.8.\n\nThe low reverse biased diode leakage current represent an advantage of silicon sensors.\n\nThis leakage current is caused by electron-hole pairs being thermally generated in the the\n\n52\n\n\n\n2.2. RADIATION DETECTION\n\nFigure 2.8 – Schematics of ionizing particle detection in a reverse biased diode. The\nfree electron hole pairs produced by the particle energy loss drift in the electric field and\nproduce a current in the diode. Thermally generated (green boxes) are also generated in\nthe depleted zone of the diode.\n\ndepleted zone of the diode. These pairs also induce signal on the read-out electrode and\n\nare emitted randomly thus subject to statistical variations. This increases the noise in the\n\nsensor and low leakage current present in reverse-biased diodes makes it a perfect detection\n\nmedium for ionizing radiation.\n\n2.2.2 Signal formation\n\nThe signal induced on a readout electrode is not due to the collection of the free carriers\n\nthemselves. The charge’s electric field flux inside the readout electrode varies as the charge\n\ndrift into the sensor and displacement current is created and generate the detectable signal.\n\nThe real signal can be calculated using the Ramo theorem [19], as shown in 2.23.\n\nQk =\n∑\ni\n\nqiφk(~rifinal)−\n∑\ni\n\nqiφk(~ri0) (2.23)\n\nWhere Qk is the charge induced on electrode k, qi is the charge of the carrier i, ~ri its\n\n53\n\n\n\n2.3. THE HYBRID PLANAR PIXEL SENSOR\n\nposition and φk the Ramo potential of electrode k. The Ramo potential is calculated by\n\nsolving the Laplace’s equation in the geometry of the detector while imposing a Dirichlet\n\nboundary condition at the electrodes, with φ = 1 at the k’th electrode and 0 at the other\n\npresent electrode and a Neumann boundary condition (~∇φ · ~n = 0, ~n is the vector normal\n\nto the boundary) at the rest of the boundaries. This theorem can be applied to a variety\n\nof detection medium ranging from plasma to solid-state detectors. The presence of space\n\ncharge do not influence the calculation of the Ramo potential [20; 21]\n\n2.3 The Hybrid Planar Pixel Sensor\n\nThe planar pixel sensor (figure 2.9) consists of an array of small rectangular diodes\n\nbuilt by implanting dopant in a Silicon wafer to form junctions. A structure of guard rings\n\nis disposed around the array of pixels to insure a smooth transition of the surface bias\n\npotential from the active area to the edge of the sensor. Each of the concentric rings is\n\nself-biased with the inner ring taking the pixel or backside electrode potential and the outer\n\nring taking the edge potential, usually the same as the verso of the edge. Details on the\n\nguard ring structure will be given in chapter 3. Lithography methods are used to create\n\nthe individual pixels diodes and contact electrode. An implant and a metallization on the\n\nbackside of the wafer is created with a dopant type opposite of the pixel’s implant type.\n\nThis create an ohmic contact. Bias is applied between each side of the wafer to deplete the\n\nsensor and create an electric field in the bulk of the sensor allowing the drift of free carriers\n\ngenerated by ionizing particles. The carrier cloud generated is localized in a small region\n\naround the particle track allowing to measure the particle position in the sensor using\n\nsignal induced on the individual pixels. An integrated circuit, called a front-end, is also\n\nbuilt with individual cells of signal lecture and digitization electronics matching the pixels\n\non the planar sensor. The integrated circuits individual channels are coupled to the diodes\n\nusing the bump-bonding technique. Each channel can then be read individually in a digital\n\nformat to obtain the information on the position and energy of the detected particles. The\n\nassembly is then mounted on a PCB or a Flex-Hybrid containing the circuits to bias the\n\nsensors and the integrated circuits and readout and transmit the acquired data. The final\n\nassembly is call hybrid Planar pixel sensor module and can be used to form large system\n\n54\n\n\n\n2.3. THE HYBRID PLANAR PIXEL SENSOR\n\nof particle tracking used in large HEP experiments such as ATLAS.\n\nFigure 2.9 – Schematics of the hybrid planar pixel sensor.\n\nPlanar pixel sensors can be used in 4 configuration of implants and bulk with each\n\ntheir advantage and inconvenient. Figure 2.10 describe the possible geometries of pixel and\n\nguard ring that are suitable for pixel sensors.\n\nThe electrode readout implant type is chosen to select the main signal carrier of the\n\nsensor. N implant are used for readout of electrons which travel faster is silicon due to\n\ntheir higher mobility. They are favored for radiation hard sensor as they are less prone\n\nto trapping. Bulk type is chosen as a function of desired guard ring side and radiation\n\nhardness issues. As N type bulk invert to P type-like bulk and depletion voltage rise after\n\nirradiation due to space charge sign inversion, the depleted zone, which expands from the\n\npn junction side towards the other junction, could not be maintained for p-in-n and p-in-p\n\nsensors when depletion voltage gets to high for the power supply. For n-in-n sensors, space\n\ncharge sign inversion is actually beneficial as the detector is always depleted under the\n\nreadout electrodes. Before inversion, depletion voltage is reduced with regard to the initial\n\nvalue hence the sensor can always be fully depleted. N-in-p sensors are unaffected by this\n\n55\n\n\n\n2.4. OTHER SILICON SENSORS\n\n(a) n-in-n sensor (b) p-in-n sensor\n\n(c) n-in-p sensor (d) p-in-p sensor\n\nFigure 2.10 – Schematics of the possible pixel sensor implant and bulk configurations.\n\nas the depletion always occurs from the pixel side of the sensor. Finally, the guard ring,\n\nto be functional, must be located on the pn junction side of the sensor. In the n-in-p and\n\np-in-n sensor case, this mean some high voltage, coming from the backside, will be present\n\nin the guard ring region and might be detrimental to the chip also located on the readout\n\nelectrode side. However, in the case of these detector, all lithography to build the electrode\n\nstructure is located on the same side of the wafer, reducing the fabrication cost of the\n\ndetectors. Table 2.2 summarized the characteristics of each configuration.\n\nThe ATLAS pixel detector is built with n-in-n planar pixel sensors. This technology\n\nwas chosen for it many advantages and is the planar pixel sensor candidate technology for\n\nthe Insertable b-layer.\n\n2.4 Other Silicon sensors\n\nIn the recent years, new technologies have been developed to build pixel sensors for par-\n\nticle detection. The goal of these new technologies is to increase the signal formation speed,\n\nreduce inactive zones of the sensor, build thinner sensor and increase radiation hardness\n\n56\n\n\n\n2.4. OTHER SILICON SENSORS\n\nTable 2.2 – Summary of planar pixel sensor configurations\n\nSensor Type Advantages inconvenients\nn-in-n\n\n– electron signal\n– inversion of depletion direction\n\nafter SCSI\n– guard rings on backside\n\n– double-sided process\n\nn-in-p\n– electron signal\n– no SCSI\n– single-sided process\n\n– guard ring on pixel side\n\np-in-n\n– single-sided process – guard ring on pixel side\n\n– hole signal\n– hard to fully deplete after SCSI\n\np-in-p\n– no SCSI\n– guard rings on backside\n\n– hole signal\n– hard to deplete with increasing\n\nradiation\n– double-sided process\n\n57\n\n\n\n2.4. OTHER SILICON SENSORS\n\nfor applications where such characteristics are required. The two main new technologies\n\nare the 3D pixel sensor and the High Resistivity Monolithic Active Pixel Sensor (MAPS).\n\n2.4.1 the 3D pixel sensor\n\nThe main difference between the planar pixel sensors and the 3D variety is the orien-\n\ntation of the electrode implant in the wafer. Through chemical etching techniques, deep\n\nholes are created in the wafer and implants are created on the surface of the holes, forming\n\np-type and n-type columns in the wafer. They are then filled with a conductive material\n\nto form the anodes and the cathodes. The bias is applied between the two type of columns\n\nand drift of the carriers occurs laterally, as shown in figure 2.11. For sensors with a pixel\n\npitch smaller than the wafer thickness, this leads to a faster signal and smaller depletion\n\nvoltage. The possibility to bias literally avoid the problem of high voltage distribution at\n\nedges present in planar pixel sensors, discussed in chapter 3 of this thesis, allowing for small\n\ninactive edges. Finally , the short drift distances of the carriers reduce the signal loss due\n\nto trapping during long charge drift makes the 3D sensors more radiation hard by design,\n\nsince, as it will be discussed in the next section of this chapter, radiation damage induced\n\ncharge loss by trapping of carriers in discrete energy levels present in the band-gap of the\n\nsensor’s material.\n\n2.4.2 High Resistivity Monolithic Active Pixel Sensors (MAPS)\n\nMAPS sensors are built on a single high resistivity silicon wafer where a low resistivity\n\nsilicon layer have been grown by epitaxy. A small subsection of the pixel surface is occupied\n\nby a detection diode depleting in the high resistivity buried layer, while the rest of the\n\nsurface is occupied by CMOS electronics in the low resistivity bulk, forming the readout\n\nelectronics of the diode, as shown in figure 2.12. A small electric field is present in the\n\ndepleted region and a small signal is generated in the detection diode. The signal is then\n\namplified and digitize on the same wafer by the readout electronics. The presence of the\n\namplifier so close to the detection diode allows low noise operation even with the small\n\nsignal produced by the passage of a particle. The presence of the electronics on the same\n\nwafer used for detection eliminated the need for a front-end integrated circuits and the\n\n58\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nFigure 2.11 – Schematics of the hybrid 3D pixel sensor.\n\nsmall thickness of the epitaxial layer and detection layer (down to 50 µm) allows to build\n\nvery thin sensors for low material budget applications.\n\n2.5 Radiation damage in Silicon sensors\n\nSilicon sensors can be damaged by the exposure to radiation. Several effects need to\n\nbe taken into account to design radiation hard sensors for use in harsh environment such\n\nas in ATLAS inner detector. Two kind of radiation damage are important in the case of\n\nsilicon sensors : Non-ionizing and ionizing energy loss by particles interacting with the\n\nsensor’s material. Each effect lead to specific changes in the sensors operation conditions\n\nand electrical characteristics.\n\n2.5.1 Non-ionizing Energy Loss (NIEL)\n\nExposure of planar pixel sensor to non-ionizing energy loss from protons, pions and\n\nneutrons modify its electrical properties in the following ways :\n\n– Space-Charge Sign Inversion (SCSI)\n\n59\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nFigure 2.12 – Schematics of the High Resistivity Monolithic active pixel sensor (MAPS).\n\n– Modification of full depletion potential (Vfd) (high voltage operation after high-dose)\n\n– Increased trapping\n\n– Leakage current increase\n\nNIEL is usually expressed, for silicon sensors, in 1 MeV neutron-equivalent by square\n\ncentimeter, (neq/cm\n2). The energy loss by exposure to different particle types and energies\n\ncan be calculated by scaling the flux of the incoming particle on the sensor surface by the\n\nratio between the NIEL in the bulk for that particle and the NIEL for a 1 MeV neutron. A\n\nlarge number of publication measuring the NIEL scaling factor for various type of particle\n\nis available in the litterature.\n\nSCSI effect was first predicted then measured experimentally [5; 6]. It is expressed\n\nthrough an inversion of the space charge sign in the depleted region of silicon sensors. This\n\ninversion has been shown to be due to the introduction of electrically active defects in the\n\nbulk that compensate for the natively present defects. This leads to an apparent change of\n\nthe bulk type, changing the wafer side where the high electric field is present and eventually\n\nleading to a complex distribution of space charge in the bulk leading to the formation of\n\n60\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\na high field region on both side of the silicon wafer (N+ − p − n − p+ structures). From\n\nequation 2.5, we can observe that an apparent charge in the bulk’s acceptor or donor net\n\nquantity will also lead to a variation of the depletion voltage needed to fully deplete the\n\nsilicon sensor. The steady introduction of these electrically active defects will eventually\n\nlead to an increase of the need depletion voltage and may limit the capacity to deplete the\n\nsensor completely.\n\nSignal carriers are subject to recombination with the same probability as thermally\n\ngenerated carriers. The introduction of defects in the bulk will lead to a lower characteristic\n\nrecombination time of the free carriers through these defects. If this time is of the order, or\n\nlower than expected collection time, it will lead to a reduction of the collected charge with\n\nregard to the expected value before the introduction of the defects. This radiation damage\n\neffect will lead to lower detection efficiency, reduced resolution, lower signal to noise ratio\n\nand will force frequent recalibration of the sensor’s readout electronics.\n\nFinally, the traps in the band-gap are possible mediator for generation and recombina-\n\ntion of carriers in indirect band-gap semiconductors. The addition of new trap in the sensor\n\nmaterial will then lead to an increased probability of thermal emission of carriers leading\n\nto an increased leakage current. This represent an issue reducing the detector performance\n\nby increasing the noise and can lead to cooling problems and thermal runaway as the dissi-\n\npated energy becomes more important. Finally, if the current leaking through the sensor is\n\ntoo important for the front-end readout out current compensation circuit capacity , it can\n\nlead to non-linear behavior of the electronics reducing the detector overall performances.\n\nFor a trap t, introduced by radiation or present in the original detector material, four\n\nprocesses can be enhanced : hole capture (Rth), hole emission (Gth), electron capture (Rte)\n\nand electron emission (Gth). Equations 2.24,2.25,2.26 and 2.27 ([17]) show the rate of each\n\nthese processes as a function of the trap’s capture cross-section (σtp,n), thermal velocity\n\n(vth), trap density (Nt), free carrier concentration (p, n),hole or electron emission probabi-\n\nlity (εtp,n) and occupancy of the trap (Pt).\n\nRth = vth,pσ\nt\nppNtPt (2.24)\n\n61\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nGth = vth,pε\nt\npNt(1− Pt) (2.25)\n\nRte = vth,nσ\nt\nnnNt(1− Pt) (2.26)\n\nGte = vth,nε\nt\nnNtPt (2.27)\n\nAt equilibrium, the sum all recombination and generation rates must equal 0 (equation\n\n2.28). In this state, some concentration of defects can remain charged and modify the space\n\ncharge distribution in the sensor, as seen in the SCSI effect. An increased trap density\n\nlead to bigger generation term and higher leakage current in reverse biased diode where\n\nrecombination terms are kept low due to low free carrier density.\n\n∑\nt\n\nGte,h =\n∑\nt\n\nRte,h (2.28)\n\nDuring collection time in the depleted zone of a reverse biased diode where carrier\n\ndensity is low, a quasi-static approximation, as shown in equation 2.29 can be used to\n\ndetermine the behavior of the signal’s carriers during charge collection. This model supposes\n\nno carriers exchange between the different defects present, which is a valid approximation\n\nif defect density is low. The localized higher carrier density (n, p) in the charge cloud\n\ngenerated by the energy deposition of a particle, recombination terms are enhanced and\n\nlead to trapping of the signal if a large density of trap is present. A radiation damage\n\nincrease, more trapping will occur and lead to lower charge collection efficiency.\n\ndn, p\n\ndt\n=\n∑\nt\n\nGte,h −\n∑\nt\n\nRte,h (2.29)\n\nFor a distribution of carriers drifting in a reverse biased diode, one can compute the\n\ncurrent induced on an electrode w using Ramo’s field (~Φw), as a function of genera-\n\n62\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\ntion/recombination rate, as shown in equation 2.30.\n\nIw(t) =\n\n∫\nvolume\n\nρ(~x,\n∑\nt\n\nRte,h(~x),\n∑\nt\n\nGte,h(~x))~∇Φwd~x (2.30)\n\nThis equation is however complex to solve and one can approximate the collected charge\n\nby supposing a uniform distribution of traps, a constant electrical field and a punctual\n\ncharge, neglecting generation of carriers. The recombination rate of the traps can be then\n\nexpressed in term of the trap characteristic lifetime (Re, h = 1/τ e,ht ). An average cha-\n\nracteristic lifetime can be calculated using equation 2.31 and the collected charge can be\n\nexpressed using equation 2.32.\n\n1\n\nτ\n=\n∑\nt\n\n1\n\nτt\n(2.31)\n\nQf =\n\n∫ tf\n\nt0\n\nQ0µe,h ~Ee\n−t/τ ~∇Φw(Q0µe,h ~Et)dt (2.32)\n\nThe hadronic interactions of particles with the atoms of the crystal lattice transfers to\n\nthem part of their kinetic energy and displaces them from their original position, creating\n\ndisorder in the crystalline structure. The displaced atoms can be moved to interstitial\n\nspace in the lattice, forming a defect, called interstitial defect, than can become electrically\n\nactive and modify the band structure of the silicon. The vacancies in the lattice left by the\n\nknocked-off atom can also create an electrically active defect called the vacancy defect.\n\nMoreover, the pre-exisisting defects and dopants in the silicon bulk can interact with\n\nthe radiation induced defects to form more complex hybrid defects with different electrical\n\nbehavior. The ROSE [22; 23; 24] and RD50 CERN collaboration [25; 26; 27; 28; 29] have\n\nworked to identify the defects that are important to understand the effects of non-ionizing\n\nradiation damage in silicon . The main important defects introduced in silicon by irradiation\n\ncan be found in table 2.3. The introduction rate of these different defects vary with the type\n\nand energy of the particle causing the damage, the present concentration of Oxygen in the\n\nbulk and of the thermal history of the silicon sample. Defect engineering can be performed\n\nby favoring the formation of non electrically active defects such as the (V −O2i) defect which\n\n63\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nis enhanced by the presence of Oxygen dimers (O2i). Oxygenated high resistivity silicon\n\nexhibit a high concentration of such dimer and therefore can substain higher radiation doses\n\nbefore effects detrimental to the operation of the planar pixel sensor becomes significant.\n\nTable 2.3 – Important defects introduced by NIEL in silicon ([17; 27])\n\nDefect type Charge state Energy level (eV)\nInterstitial (I) I− EC − 0.39\n\nI0\n\nI− EV + 0.4\n\nVacancy (V) V −− EC − 0.09\nV − EC − 0.4\nV 0\n\nV + EV + 0.05\nV ++ EV + 0.13\n\nDivacancy (V2) V −−\n2 EC − 0.23\nV −\n\n2 EC − 0.39\nV 0\n\n2\n\nV +\n2 EV + 0.21\n\nA-Center (V-O) (V −O)− EC − 0.18\n(V −O)0\n\nDivacancy Oxygen complex (V2 −O)\nVacancy Oxygen dimer complex (V −O2i) (V −O2i)0\n\nThe modeling of the complex chemistry of defects in irradiated silicon can be quite\n\ncomplex and unpractical for the modeling of irradiated pixel sensors. However simple pa-\n\nrametrization reproducing well the different known effects observed in silicon has been\n\ndevelopped in the recent years. A deep acceptor and a deep donor are introduced in the\n\nbulk to account for space charge sign inversion (SCSI), double junction effects [5; 6] and\n\nleakage current increase [5; 6; 30; 31]. In addition, to account for trapping and recombi-\n\nnation of carrier induced by radiation damage, a shallow hole and electron trap must be\n\nadded. The model we used is shown in tables 2.4 and 2.5 , based on the latest results from\n\nRD50 collaboration [29] and work of several groups [32; 33; 34].\n\n64\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nTable 2.4 – n-type radiation damage model\nDefect’s energy\n(eV)\n\nIntroduction rate\n(cm−1)\n\nElectron capture\ncross-section (cm−2)\n\nHole capture cross-\nsection (cm−2)\n\nEc − 0.42 13 2.2e-15 1.2e-14\nEc − 0.53 0.08 4e-15 3.5e-14\nEc − 0.18 100 1e-14 1e-16\nEv + 0.36 1.1 2e-18 2.5e-15\n\nTable 2.5 – p-type radiation damage model\nDefect’s energy\n(eV)\n\nIntroduction rate\n(cm−1)\n\nElectron capture\ncross-section (cm−2)\n\nHole capture cross-\nsection (cm−2)\n\nEc − 0.42 1.613 2.e-15 2e-14\nEc − 0.46 0.9 5e-15 5e-14\nEc − 0.10 100 2e-15 2.5e-15\nEv + 0.36 0.9 2.5e-14 2.5e-15\n\nThese parameters must be adjusted using a fit method to determine adequate intro-\n\nduction rates, level and capture cross-section for each type of silicon used.\n\nA simpler parametric model has been developed by the ROSE and RD50 collaboration\n\nto evaluate the evolution of leakage current, average trap characteristic lifetime and net\n\ndopant/defect concentration in irradiated sensors. The Variation of leakage current density\n\nin the bulk of a depleted sensor is parametrized following equation 2.33\n\n∆Ivol\nV\n\n= αΦ (2.33)\n\nWhere Ivol is the volume generated current, V the bias potential, α the leakage damage\n\nconstant and Φ the exposed fluence in neq/cm\n2. The average trap characteristic lifetime\n\ncan be also be expressed as a function of fluence as shown in equation 2.34, where τt0 is\n\nthe original trapping time.\n\n1\n\nτt(Φ)\n=\n\n1\n\nτt0\n+ βΦ (2.34)\n\nFinally, the net dopant/defect concentration is expressed following equation 2.35, where\n\nND0 is the initial donor concentration,NA0 the initial acceptor concentration andN the net\n\ndopant concentration. From N, it is possible to compute the depletion potential following\n\n65\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\nequation 2.5. Acceptor removal and donor creation have not been observed experimentally,\n\nexplaining the absence of such term in this equation. 0\n\nN(Φ) = ND0e\n−cΦ −NA0 − bΦ (2.35)\n\nTable 2.6 show typical value for the radiation damage constants of this model.\n\nTable 2.6 – Typical radiation damage constants [35]\n\nconstant value\nα 8.0× 10−17 Acm\n\nβ 0,24 ×10−6 cm2s−1\n\nc 3.54 ×10−13 cm2\n\nb 7.94 ×10−2 cm−1\n\n2.5.2 Ionizing energy loss\n\nSilicon dioxide present at the surface is the main material damaged by ionizing energy\n\nloss . The dose of ionizing energy loss radiation damage is usually expressed in Rad, which\n\nrepresent 6.24× 1010 MeV of ionizing energy deposition per kilogram of material.\n\nSiO2’s Oxygen valence electrons present at the interface and uncompensated by a Si-\n\nlicon atom create local traps for holes, as shown in figure 2.13. Holes from electron-hole\n\npairs generated by ionizing particles crossing the oxide can be trapped in this layer. Elec-\n\ntrons have higher mobility (20 cm2/V s) than holes (2× 10−5 cm2/V s) in SiO2 and collect\n\nrapidly while holes accumulates in the traps present near the interface [36]. The electrical\n\nfield created by this sheet of positive charge attracts silicon’s free electrons that then for\n\na compensating layer of free carrier at the Si-SiO2 interface. Few carriers can cross the\n\ninterface potential barrier by tunneling and recombine with their opposite carrier, leaving\n\nthe charge layer, called inversion layer, almost permanently at the interface.\n\nThe charge density at the interface is known to vary almost linearly with exposed\n\nfluence from 1011 cm−2 to 1012 cm−2 between 0 and 1 × 108 Rad, usually equivalent\n\nin ATLAS inner detector to exposition to a NIEL of 0 to 1 × 1015 neq/cm\n2. We then\n\nconsider the charge layer to be saturated for higher fluences, as observed experimentally\n\n66\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\n[37]. Oxide charge saturation concentration is process dependent [38] so the saturation\n\nfluence and charge concentration can be chosen for modelisation to be coherent with the\n\ndata presented in literature.\n\nFigure 2.13 – Schematics of radiation damage effect at the silicon-silicon dioxide interface\nin silicon sensors.\n\nThe presence of this electron layer at the interface can form a conductive path between\n\ndifferent electrodes, increasing crosstalk and leading to unwanted parasitic leakage path in\n\nthe sensor. Mitigation methods to prevent this effect will be presented in next chapter.\n\n67\n\n\n\n2.5. RADIATION DAMAGE IN SILICON SENSORS\n\n68\n\n\n\nChapitre 3\n\nTCAD Simulation models\n\nTechnology Computer-Assisted Design (TCAD) uses our present knowledge of the par-\n\ntial differential equations describing charge carrier’s motion and interactions with the crys-\n\ntal lattice in semiconductors, detailed in equations 2.2, 2.3 and 2.4 coupled to finite element\n\nmethod to simulate the electrical parameters of the device. Finite element method use a\n\nlinearized version of the transport equation to describe the problem in terms of a linear\n\nsystem of equation that can be solved by linear algebra methods. To obtain a solution to\n\nthe variables of the transport equations (n,p,V) in a arbitrary geometry, we must subdivide\n\nthe surface or volume in rectangular, triangular, prismatic or pyramidal sub-elements small\n\nenough that the solution in locally polynomial in this domain and can be approximated by\n\na polynomial Φ. The sum of all sub-elements covering the simulation geometry is call the\n\nmesh, as seen in the example for a simple geometry in figure 3.1.\n\nTo obtain a good approximation of the solution using this method, mesh element’s\n\ndomain size must be chosen to be sufficiently small to be able to do this approximation.\n\nRegion where the solution is expected to vary rapidly must be subdivided in small region\n\nuntil the solution can be represented as a locally polynomial function. TCAD software\n\nare bundled with meshing algorithm that can use know quantities in the geometry, such\n\nas impurity concentration, to generate the sub-elements covering the domain to simulate.\n\nHowever, no perfect method exist to determine the perfect mesh and case-by-case study of\n\nthe mesh to use must be perform to ensure the validity of the solution obtained over this\n\ndiscretization. The solution to the equations, once meshing and interpolation equations are\n\n69\n\n\n\nFigure 3.1 – Meshing of a disc surface using triangular sub-elements\n\nchosen, can then be expressed as :\n\nV, p, n =\n\nn∑\ni\n\naV,p,ni Φi (3.1)\n\nWhere i is the indice of an intersection of the sub-elements. The function Φ are usually\n\nchosen to be equal to 1 at element intersection i and 0 at all other surrounding intersection.\n\nFigure 3.2 show an example of functions Φ that can be selected in a simple 1D geometry\n\nto approximate a semi-spherical function.\n\nThis method can be used to explore different designs of semiconductor detectors before\n\ntheir production and optimize its electrical parameters. We used this method to explore\n\ndifferent possible designs for the IBL and super-LHC ATLAS planar pixel detector. The\n\ninfluence of many design parameters as the number and spacing of guard rings, sensor’s\n\nthickness and inactive edge width on sensor electrical characteristics have been simulated,\n\ngiving insight on the effects of sensor design and processing on the devices performances.\n\nRadiation damage can also be included in the physical model used in the simulation,\n\nallowing to extract macroscopic effects caused by change in bandgap structure detailed in\n\n70\n\n\n\n3.1. PROCESS SIMULATION\n\nFigure 3.2 – Simple linear interpolation function used to approximate a function in 1D\n\nchapter 2.\n\n3.1 Process simulation\n\nThe first step to obtain a realistic simulation of a sensor is to obtain an accurate\n\ndescription of its geometry. Most TCAD simulation software include a process simulation\n\npackage that allow to simulate the fabrication process of silicon sensors. The main step to\n\nproduce a realistic process simulation of a pn junction are as follow :\n\n1. Oxidation\n\n2. Nitride deposition and etching\n\n3. Insulation implantation\n\n4. Oxide etching\n\n5. Implantation\n\n6. Thermal annealing\n\n7. Via etching and electrode deposition\n\n8. Passivation and passivation etching\n\nIn typical silicon detectors, an additional implant must be created to insulate the elec-\n\ntrodes from each other before creating the main implant. As it was seen in chapter 2,\n\n71\n\n\n\n3.1. PROCESS SIMULATION\n\nionizing radiation damage cause the formation of an inversion layer at the silicon-oxide in-\n\nterface that can shortcut the different implants. To prevent this, an low dose p+ implant is\n\ncreated on the n+ side of the pixel sensors. This implant’s excess of free holes will compen-\n\nsate the inversion layer created by the accumulation of electrons and interrupt the channel\n\nthat forms at the surface. Three type of insulating implants are used in the industry. The\n\nmost simple is the p-spray technique, which consist in the implantation of a uniform p\n\ndose across the full wafer. A different technique , called p-stop, use an additional nitride\n\nlayer deposited over the oxide layer to stop the dopant ion beam and create segmented p+\n\nimplants where nitride was etched between the main n+ ones. Finally, an hybrid method,\n\ncalled moderated p-spray, combines both method. The thickness of the layer of nitride is\n\nchosen to let some of the ions reach the silicon, creating a uniform implant across the wa-\n\nfer and stronger and deeper implants where nitride was etched, between the n+ implants.\n\nFigure 3.3 shows the two first type of insulation. The moderated p-spray is simply a combi-\n\nnation of both method. The insulation implants form junctions with the main n+ implant\n\nand high electric field will be present in this region. Each method need to be optimized\n\nto avoid creating high electric field that may lead to breakdown while providing sufficient\n\ninsulation for operation after irradiation.\n\nThe second step in the process consist in heating the bare wafer of silicon in a oxygen\n\natmosphere to grow a layer of silicon dioxide at the surface of the wafer (O(200 nm))\n\n. This layer is then etched down to a very thin layer (O(40 nm)) using lithography, to\n\nform the shape of the implants. A beam of mono-energetic ions (O(10− 120 keV )) is then\n\ndirected to the surface of the wafer and a fixed dose of ion by surface unit (O(1015 cm−2))\n\nis delivered. In the area where a thick film of oxide has been left, the stopping power of the\n\nlayer is sufficient to completely absorb the incoming ions while in the etched region, ions\n\nwill travel into silicon forming an implant with a shape and magnitude determined by the\n\nimplantation dose, the beam energy and orientation and the thickness of the oxide layer\n\nleft in the implant area.\n\nOne all implants have been created, the wafer must undergo an annealing step, where it\n\nis heated for sometime at very high temperature. This step is needed to activate the dopant\n\nintroduced in silicon. To be electrically active, dopant atoms must be correctly placed in\n\n72\n\n\n\n3.1. PROCESS SIMULATION\n\n(a) p-spray insulation\n\n(b) p-stop insulation\n\nFigure 3.3 – The two main type of implant insulation used in pixel sensors.\n\nthe crystal lattice. The heating process gives the kinetic energy to the dopants to diffuse\n\nthrough the bulk and react with the lattice structure to become electrically active. During\n\nthis process, the shape of the implant is modified as dopants migrate by diffusion further\n\ninto the silicon bulk. The annealing temperature and time are important parameters to\n\ndetermine the final shape of the junction.\n\nThe final step of the process consist in opening small holes, called via, in the oxide in\n\nthe n+ implant area reaching the surface of the silicon (nitride has previously been opened\n\nin this area to allow access to the oxide at this step). An aluminum layer (O(800 nm))\n\nis then deposited and etched outside electrode area to produce the electrical contacts to\n\nthe n+ implants. Finally, a thick layer of a passivating material is deposited over the area\n\nuncovered by the electrodes to provide a good protection of the surface.\n\n73\n\n\n\n3.2. DEVICE SIMULATION\n\nThe parameters of the process affecting the implant profile need to be known to create\n\naccurate representation of the device we wish to simulate. The process details we use in\n\nour simulation have been obtained through discussion with designers and manufacturer\n\nof silicon devices. Some parameters are however hard to determine from accessible data\n\nand are not disclosed by the manufacturer. These values can however be obtained through\n\nexperimental methods as will be shown in the rest of this chapter.\n\n3.2 Device simulation\n\nDevice simulation is used to obtain electrical parameters of a geometry we built through\n\nprocess simulation. For a device simulation, the geometry to be simulated must be carefully\n\nchosen to avoid increasing the computational complexity of the problem to be solved.\n\nBoundary conditions must also be selected to represent the operation conditions of the\n\ndevice.\n\n3.2.1 Geometry\n\nFull simulation of a pixel sensor is impossible to perform in modern computers as\n\nthe number of mesh point needed to accurately describe the solution to the equations in\n\nthe three-dimensional domain is too important to be handled by computers. Simulation\n\nperformed in the next section are done in two dimension to reduce the simulation time.\n\nPeriodicity and symmetry of the geometry can be exploited to reduce the size of the problem\n\nto be solved. Figure 3.4 shows how pixel sensor quasi-periodicity and symmetry can be used\n\nto define a two-dimensional geometry that can be simulated with a TCAD software. We\n\nconsider a Y-Z oriented cut plane in a semi-infinite sensor with its guard ring structure\n\nand cutting edge and a plane of pixels extending extending infinitely in the ±X and +Y\n\ndirection. This represent a good approximation for a pixel sensor far from the corners of\n\nthe device. The edges not represented in the two dimensional representation are supposed\n\nto not interfere with the local electrical behavior of the simulated geometry.\n\nTo obtain the solution to the differential equations describing the charge transport and\n\nPoisson equation, we must provide the boundary conditions as fixed values of the variables\n\n74\n\n\n\n3.2. DEVICE SIMULATION\n\nto be solved, the electron and hole concentration (n,p) and the electrostatic potential(V)\n\nor their derivative, current densities ( ~Jp,n) and electric field ( ~E). Real operation conditions\n\nof the simulated sensors cannot be completely described in this manner and approximation\n\nmust done to obtain a solvable problem. The simulation geometry must be selected to allow\n\na solution that represent correctly the real operation conditions.\n\nFigure 3.4 – Simplification of a n-in-n planar pixel sensor geometry for TCAD simulation\n\n3.2.2 boundary conditions\n\nTo solve our set of differential equations we need to restrict ourselves to a solution\n\nin a bounded domain, the sensor. We must choose boundary conditions reflecting the\n\nproperties of the system we want to simulate. Three types of boundaries were used during\n\nour simulation, representing the oxide-silicon interface, the electrode interface, and the\n\nperiodicity boundary. In addition we need a model for the cutting edge of the sensor.\n\nThe boundaries between silicon dioxide and silicon is a semiconductor/insulator boun-\n\ndary characterized by the presence of an accumulated charge layer at the interface. The\n\n75\n\n\n\n3.2. DEVICE SIMULATION\n\nboundary condition applied to these surfaces for the Poisson equation is the Neumann\n\nboundary condition (3.2) that takes into account the charge layer density (ρs) present at\n\nthe surface . Also, electrons and holes concentrations are set to zero on this boundary and\n\nthe current is not allowed to flow through this surface.\n\nn̂ · ε1~∇Φ1 − n̂ · ε2~∇Φ2 = ρs (3.2)\n\nMetal-semiconductor surfaces are the boundaries between the silicon bulk and the me-\n\ntallic electrodes. This is usually a ohmic contact and the current is allowed to flow through\n\nthem. The voltage Φ is constant and equals the bias voltage applied to the sensor by an\n\nexternal power supply. The concentration of carriers (ps,ns) at the surface of the contact is\n\ndetermined by equations (3.3), (3.4), derived for Boltzmann’s statistics, knowing the bias\n\nvoltage applied at the electrodes. The effect of the contact work function is considered\n\nnegligible as highly doped regions are located below the electrodes.\n\nns =\n1\n\n2\n[(N+\n\nD −N\n−\nA ) +\n\n√\n(N+\n\nD −N\n−\nA )2 + 4n2\n\ni ] (3.3)\n\nps =\nn2\ni\n\nns\n(3.4)\n\nWhere N+\nD , N\n\n−\nA are the ionized donors concentration and ionized acceptors concentra-\n\ntion in cm−3.\n\nGuard ring structures are metal semiconductor interfaces where the metallic electrode\n\nself-biased. To represent this case, we must impose a null current flow on this contact. The\n\nbias voltages taken by the floating contacts are then found by the solver of the TCAD\n\nsoftware.\n\nTo reduce the size of the problem to be solved, we can use periodicity boundary condi-\n\ntions using geometric properties of the sensor. In our simulation, we will be interested to\n\nthe solution on the sides of the sensor. Knowing the solution will become quasi-periodic\n\nin the X-Y plane when approaching the center of the device. Far from the edge , we can\n\ntruncate our model at a distance large enough to consider the solution will become as if it\n\n76\n\n\n\n3.2. DEVICE SIMULATION\n\nwas periodic at this point. We then impose the periodicity condition (3.5) at the surface\n\nfor electrons and holes concentration and for the bias voltage.\n\n~∇V · n̂ = 0\n~∇n · n̂ = 0\n~∇p · n̂ = 0\n\n(3.5)\n\nWhere n̂ is the unitary normal vector of the boundary. Physically, this represent the\n\ncondition where no current is flowing out or in the geometry and no electric field lines flow\n\nout of the simulated boundary.\n\nThe dicing of pixel sensors from their originating wafer creates structural damage that\n\naffects the properties of the edge. A dead edge width must be included in the design to\n\nexclude this zone from the sensible part of the sensor. This dead edge is added to inactive\n\npart of the sensor and must be kept as small as possible.\n\nA special attention must be taken to model the cutting edge of a silicon sensor. Dicing\n\nmechanism induces structural damages in the Silicon crystal lattice near the cutting region.\n\nThis induces a process of amorphization of silicon. Amorphous silicon is a complex material\n\nwhere no short or long distance orders exists in the crystal lattice. A method to model\n\namorphous silicon is to introduce a high number of defects in the band gap of Silicon. As\n\nthe crystal lattice of the Silicon is highly perturbed in the cutting edge region, trap states\n\nare created by the defects in the crystal lattice that are introduced. To represent such a\n\ndistribution of defects in the band gap, we use a continuous density of states distribution\n\nto describe the band gap defects distribution. This distribution can then be tuned to reflect\n\nthe behavior of real sensors measured in the laboratory. The generation-recombination term\n\nrelated is calculated using an integral form of equation 2.10. Equation (3.6)[39] shows how\n\nwe describe the defect distribution in the band gap.\n\ng(E) = gTA(E) + gTD(E) + gGA(E) + gGD(E)\n\ntA(E) = NTAe\nE−Ec\nWTA\n\ntD(E) = NTDe\nEv−E\nWTD\n\ngA(E) = NGAe\n(\nEGA−E\nWGA\n\n)2\n\ngD(E) = NGDe\n(\nE−EGD\nWGD\n\n)2\n\n(3.6)\n\n77\n\n\n\n3.2. DEVICE SIMULATION\n\nThe density distribution function consists of two exponential tails functions (TD, TA)\n\nand two Gaussian function distributions for donors and acceptors (GD,GA) giving the\n\nenergy distribution in cm−3. Table 3.1 shows the default parameters used for this model\n\nin our simulation. The defect density distribution that is created by these parameters is\n\nrepresented in figure 3.5. Ev = −1.12 eV is the valence band energy and Ec = 0 the\n\nconduction band energy. The model used in these simulations was proposed by E. Noschis\n\nand al. [40]\n\nTable 3.1 – Default defect density of states distribution parameters in SILVACO TCAD\nsoftware\n\nParameters Values\nNTA 1.12× 1021 cm−3/eV\nNTD 4.00x1020 cm−3/eV\nNGA 5.00× 1017 cm−3/eV\nNGD 1.50× 1018 cm−3/eV\nEGA 0.4 eV\nEGD 0.4 eV\nWTA 0.025 eV\nWTD 0.050 eV\nWGA 0.100 eV\nWGD 0.100 eV\n\nFigure 3.5 – Defect density distribution in the band gap of amorphous silicon used for\nour simulation\n\n78\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n3.3 The Multi-Guard Ring structure\n\nThe goal of the guard ring structure present next to the high-voltage electrode or the\n\npixel matrix is to ensure a smooth transition from high voltage to ground while approaching\n\nthe outer edge of the device. The edge usually takes the same potential as the backside\n\nof the sensor as the high amount of defects at the cutting edge render it conductive and\n\nelectrically link the two sides together. Each guard ring acquire its bias voltage by a punch-\n\ntrough mechanism forming a smooth transition from high bias voltage to ground at the\n\nedge of the sensor. This is needed to ensure that no bias voltage difference exist between\n\nthe two sides of the wafer, close to the edge. This bias would generate excessive current\n\ndetrimental to the operation of the sensor [36]. The inner ring can also be used, if connected\n\nto bias, to collect leakage and surface current that would increase the noise when collected\n\nby the pixels. This principle is called a current-terminating structure (CTS) [40].\n\nIn ATLAS actual design of the pixel sensor, the number of guard ring is fixed to 16, with\n\nan implant width of 10 microns. The electrodes covering the guard ring implants are 16 to\n\n22 µm wide with the largest at the outer side of the structure. They overshoot the implant\n\nby 2 µm in the edge direction and 1 to 40 µm in the active area direction. Distance between\n\nthem varying from 15 to 8 µm. The guard rings represent a dead zone in pixel sensors,\n\nmeaning no particle is detected close to the structure. This corner of a IBL prototype\n\nsensor with its guard ring is represented in figure 3.6. The presence of these inactive zones\n\nsurrounding the sensor force the overlapping of the sensors in ATLAS tracker to avoid\n\ndetection gap between sensors. This overlap increase the amount of material present in\n\nthe tracker and should be avoided to reduce the material budget of the inner detector and\n\nincrease the simplicity of its configuration. Reduction of the guard ring area for the sensors\n\nto be used in IBL are a key to minimize its material budget and its inactive zones.\n\nThe goals of the simulation performed in this chapter are to evaluate the effects of\n\nmodifying the number of guard rings and their spacing factors that can reduce the dead\n\nzone while maintaining adequate operation conditions for the sensor and exploring the effect\n\nof radiation damage on the efficiency of guard ring structure. This was used to determine\n\nhow modifications to the sensor and guard rings geometry can be used to reduce inactive\n\n79\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\narea while keeping guard rings active.\n\n80\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n3.3.1 Principles of guard ring structures\n\nFigure 3.6 – Corner view of a FE-I4 n-in-p pixel sensor\nstructure showing the guard ring structure and the 3 first\ncolumn and 18 first row of pixels.\n\nThe guard rings are used to\n\ncontrol the potential drop from\n\nelectrodes in the active area of\n\nthe sensor to the cutting edge\n\nof the device, as shown in fi-\n\ngure 3.7 for the example of a\n\nn-in-n sensor. This provide a\n\nreliable way to control the po-\n\ntential at the surface of the\n\nsensor edges. Without guard\n\nrings, the potential distribu-\n\ntion at this surface would be\n\nlargely influenced by its pro-\n\ncessing quality. The presence\n\nof an inversion channel,which\n\nmagnitude is related to surface quality, render the surface conductive, and potential drop\n\nonly occur in resistive regions of the surface. The presence of defects at the interface will\n\naffect the resistivity of the surface and can be detrimental to detector operation by cau-\n\nsing the presence of sharp electric field peaks in the more resistive regions and eventually\n\nbreakdown of the sensor. The guard rings help to control the surface behavior and render\n\nthe sensor detector more stable and independent of the surface state by imposing a gradual\n\nsurface potential drop through a bulk process, independent of the surface state.\n\nFig. 3.8 shows guard ring geometry, with its metal overhangs covering the oxide. Guard\n\nrings are biased by a punch-trough mechanism creating a current circulating between the\n\nguard rings. The punch-trough occurs when the depletion region of both guard rings are\n\nin contact. This happens as the depletion region of the pixels or high voltage electrode\n\nexpands as bias increase and reach the different guard rings. The vicinity of a guard ring\n\nmetal electrode is biased at the same potential as the silicon implant region, reducing the\n\nvertical electric field in the oxide under the guard ring overhangs. This has the effect of\n\n81\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nFigure 3.7 – Guard ring electrical behavior (n-bulk)\n\ninterrupting the inversion channel formation at the Si − SiO2 interface, problematic for\n\nn- in-p sensors, as shown on figure 3.8. This channel is caused by the presence of hole\n\ntraps near the SiO2 boundary with Silicon, as explained in chapter 2. These traps become\n\npositively charged as they are filled by holes created by an incident ionizing particle or\n\nthermal generation [41]. In the case of n-in-p sensors, the guard rings are located on the\n\nsame surface as the pixels, collecting electrons. The orientation of the electric field inside\n\nthe oxide will favor the drift of holes to the silicon silicon dioxide interface and increase the\n\nmagnitude of the inversion layer charge density depending on the local current density in\n\nthe oxide. Close to the guard rings, reduced electric field will inhibit the drift of the hole\n\nin the oxide and cause this interruption of the inversion channel\n\nA set of two guard rings can be seen as a blocked MOSFET with the source connected to\n\ngate. The transistor is kept in an off state with the punch-trough voltage and gate to drain\n\nresistance determining the guard ring bias behavior. The guard ring metallic overhangs, are\n\nused to reduce the electric field present on this side of the guard ring at the interface and\n\ninterrupt the electron channel by suppressing the vertical electric field in this region. The\n\nlong overhang, oriented towards the pixel region, is used to suppress the punch-trough hole\n\ncurrent and increase the punch-trough voltage [36]. The p-spray, p-stop and moderated\n\np-spray methods are also used to mitigate the effects of the formation of this inversion\n\nchannel. A drawback of this method is the apparition of electric field peaks at the sides\n\n82\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nof the guard ring’s implant junction with the p-spray implant, making this region a weak\n\npoint for breakdown formation.\n\nFigure 3.8 – Guard ring schematic representing the interruption of the inversion channel\n(p-bulk)\n\nThe multi guard ring structure geometrical and electrical parameters such as the doping\n\nprofile of the implant, the overhangs length and the distance between guard rings influence\n\nthe electrical behavior of the guard rings. The voltage drop between guard rings will be\n\nmodified if these parameters are changed as punch-through voltage and currents will be\n\naffected by these modifications. The punch-trough mechanism occurs at depletion and\n\nguard rings become active when lateral depletion zone reach them. As we know from\n\nequation 2.5, depletion depth is proportional to the square root of the applied bias voltage.\n\nFor guard rings located close to the pixel region, the overhang directed toward the pixel\n\nmust be kept small to favor higher punch-trough currents and bigger voltage drop on shorter\n\ndistance. TCAD simulation represent the perfect tool to study the potential distribution\n\nof guard ring geometries and to optimize them to improve the performances of the pixel\n\nsensors\n\n83\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n3.3.2 Optimization of guard ring structures for reduction of inactive\narea and radiation hardness\n\nTCAD simulation focuses on comparing electrical parameters of different multi-guard\n\nring structures before and after irradiation. Figure 3.9 shows the geometry of the three\n\nstructures simulated. The first one is the ATLAS actual pixel sensor, an n-in-n structure.\n\nThe two others are n-in-p designs proposed as candidate for IBL and super LHC pixel\n\nsensor replacement. Detectors with these new multi-guard ring structure designs have been\n\nproduced in collaboration with the laboratories forming the ATLAS Planar Pixel Sensor\n\nUpgrade group. P-bulk and n-bulk productions of pixel sensors including other structures,\n\nlabeled in this document as respectively PPSU09-n and PPSU09-p, were organized and\n\nbased on the knowledge gathered from the simulation presented here and experimental\n\nstudies from collaborating laboratories.\n\nSimple 6x6 mm diodes with a number of guard rings varying from 1 to 4 were placed on\n\nthe production to help with simulation model calibration by providing a simple geometry\n\nfor comparison with TCAD results. The large size of the diode guard ring allow easy\n\nmeasurement in clean room of their potential and were also used to study the evolution of\n\nguard ring behavior for this production under irradiation.\n\nThe simulation studies performed prior to the production of the sensors [42; 43; 44;\n\n45; 46] have shown that the results of the simulation, regarding guard ring behavior and\n\nbreakdown voltage was dependent on doping profile of the implants forming the guard ring\n\nstructure and pixels. A dedicated test structure was placed on each of the PPSU09 wafer\n\nproduction to eventually measure the profile of the implants using various techniques. Re-\n\nsults of these measurements will be presented in the next chapter. Simulation presented\n\nin this section are based on the measured implant parameters for the PPSU09 production\n\nand simulation done prior to it have been reprocessed with the correct implantation para-\n\nmeters. Bulk resistivity was fixed to 5000 Ωcm for all simulation. Temperature was 300 K\n\nfor unirradiated sensors and 250 K for irradiated ones.\n\nIn this section, I present simulation performed to reduce the span of the multi guard-\n\nring structure through reduction of the number of guard rings and shift of the structure\n\nunder the pixels. Radiation damage effects on guard rings were simulated to evaluate the\n\n84\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nbehavior of the new guard ring structure under irradiation. Following the results of these\n\nsimulations, the method was used for the final n-in-n planar sensor candidate for the IBL\n\nin the PPSU09 production.\n\nFigure 3.9 – Multi-guard ring structures used for simulation, obtained from process si-\nmulation\n\nSimulation of the current ATLAS planar pixel sensor design was first performed up to\n\n500 V for unirradiated and irradiated sensors up to 1015 neqcm\n−2, the foreseen fluence for\n\nthe actual sensor in LHC. The radiation damage model presented in table 2.4 was used.\n\nFigure 3.11 shows the simulated bias voltage for the Actual ATLAS pixel design for irradia-\n\nted and unirradiated case. The irradiated case is simulated at the working operation bias\n\nvoltage of 150 V. The irradiated model simulation were performed at 500V, the maximum\n\n85\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\npossible bias voltage reachable with the current power supplies. One pixel is included along\n\nwith the guard rings (to the left of the figures). As fluence seen by the sensor increase, the\n\nspace charge sign invert and depletion occurs from pixel toward the backplane like in a\n\nn-in-p sensor design. Figure 3.10 show the potential distribution on the guard rings of the\n\nATLAS model as a function of the bias voltage. Comparison with experimental data is also\n\nshown. More details on experimental validation will be given in next chapter.\n\nFigure 3.10 – Potential distribution in the ATLAS standard n-in-n Multi-Guard Ring\nStructure, simulated and measured\n\nFigure 3.12 shows the electron concentration in the sensor for different fluences. The\n\nresults of Space charge sign inversion (SCSI) is the replacement of electrons by holes as the\n\nmajority carrier. This is shown in figure 3.13 representing hole concentration increasing as\n\nelectron concentration decrease, mainly in the undepleted region. The undepleted volume\n\nfor an unirradiated sensor extends on 900 microns from the edge of the sensor. The depletion\n\nzone never reach the edge of the device because of the large 500 µm safety edge left after\n\nthe guard rings. This edge width could be modified to reduce the inactive edge of the sensor\n\nwhile keeping a safe margin between the cutting edge and the depletion region. This width\n\nis retained until space charge sign inversion but it is then replaced by a holes undepleted\n\narea , as seen in figure 3.13.\n\nThe n-in-p structures shown in figure 3.9 were also simulated in the IBL conditions\n\n86\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) unirradiated (150 V) (b) 1014neqcm\n−2 (600V)\n\n(c) 5× 1014neqcm\n−2 (600V) (d) 1015neqcm\n\n−2 (600V)\n\n(e) 5× 1015neqcm\n−2 (600V)\n\nFigure 3.11 – Simulated 2D voltage profile for ATLAS n-in-n pixel sensor (300 µm thick-\nness, 1700µm width. Color scale in Volt)\n\n87\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) unirradiated (150 V) (b) 1014neqcm\n−2 (600V)\n\n(c) 5× 1014neqcm\n−2 (600V) (d) 1015neqcm\n\n−2 (600V)\n\n(e) 5× 1015neqcm\n−2 (600V)\n\nFigure 3.12 – Simulated 2D electron concentration profile for ATLAS pixel sensor (300\nµm thickness, 2500µm width)\n\n88\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) unirradiated (150 V) (b) 1014neqcm\n−2 (600V)\n\n(c) 5× 1014neqcm\n−2 (600V) (d) 1015neqcm\n\n−2 (600V)\n\n(e) 5× 1015neqcm\n−2 (600V)\n\nFigure 3.13 – Simulated 2D hole concentration profile for ATLAS pixel sensor (300 µm\nthickness, 2500µm width)\n\n89\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(250 microns edges, 1000V bias voltage, up to 1016 neqcm\n−2). These n-in-p guard ring\n\ndesign were proposed to reduce the span of the guard ring structure currently used by\n\nreducing the size of the different guard rings or by reducing their number of them. The\n\nfirst design uses 17 very small guard rings while the second model use 9 larger guard\n\nrings and both represent an approximative reduction of the guard ring span by 200 µm.\n\nFigure 3.14 shows the hole concentration in the small guard ring sensor at various levels\n\nof irradiation. Resistivity of the silicon is reduced by the radiation damage and depletion\n\npotential should be increased. This result in a reduction of the visible depth of the depleted\n\nregion for a given bias voltage. The depletion region never reaches the cutting edge of the\n\nsensor indicating that the 100 µm safety margin is safe enough for this type of n-in-p\n\nsensors. The large guard ring model show hole concentration distribution compatible with\n\nthe small guard ring model and is not represented here.\n\nFigure 3.16 show the distribution of potential on the guard rings of the three structures.\n\nThe unirradiated structures are biased to voltage higher than breakdown voltage to observe\n\nthe guard ring behavior and put in evidence the bend in the curves due to breakdown that\n\nshould be observable experimentally. This simulation is possible because TCAD simulation\n\ndoes not include thermal simulation. Voltage curves shown in figure 3.15 are cut before\n\nthe 1500 V bias voltage because of the divergence when simulating bias voltage much over\n\nbreakdown voltage. This is due to a hard breakdown occurring on edge pixel side generating\n\nhuge current. Guard rings are biased as the depletion zone limit approach toward them\n\nand as current begins to circulate between them by punch-trough mechanism. In n-in-n\n\nsensors, as depletion occurs from pixel side after SCSI, guard rings on the opposite side\n\nstay in the undepleted zone until full depletion of the sensor, making them ineffective to\n\nprovide the smooth surface potential drop required for operating sensors with a high full\n\ndepletion voltage. This situation also implies that a steep voltage variation is present at the\n\nsurface between the pixels (0 V) and edges (High Voltage). This eventually creates a weak\n\nspot favoring breakdown in the sensor, as observed in the Current versus Voltage curves\n\nin figure 3.15. The guard rings of the n-in-p structures have a similar behavior before\n\nand after irradiation as the depletion still occurs from the pixel side towards the high\n\nvoltage electrode side. The difference in the bias potential of each guard ring at different\n\n90\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) unirradiated (1000 V) (b) 2× 1015neqcm\n−2 (1000V)\n\n(c) 4× 1015neqcm\n−2 (1000V) (d) 6× 1015neqcm\n\n−2 (1000V)\n\n(e) 8× 1015neqcm\n−2 (1000V) (f) 1016neqcm\n\n−2 (1000V)\n\nFigure 3.14 – Simulated 2D hole concentration profile for n-in-p small guard ring type\npixel sensor (300 µm thickness, 2500µm width)\n\n91\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nfluences is due to the damage in the oxide which accumulate charge at the silicon-oxide\n\ninterface and to a modification of the position of the depletion region limits. The change\n\nin space charge density modify the effective doping concentration that determines the full\n\ndepletion potential of the sensor. As guard rings are biased by a punch-trough mechanism,\n\nthe position of the depleted region limit relative to the guard rings position determines the\n\nbias potential of guard rings as a function of high voltage bias. The bent observed in guard\n\nring potential curves at high voltage is directly related to the current being generated in\n\nthe guard ring vicinity by impact ionization and can be correlated to the breakdown in\n\nfigure 3.15.\n\nFigure 3.17 show the electric field distribution at 1 µm under the guard ring for the\n\nthree model under study at 400V. The electric field peak value are the lowest for the ATLAS\n\nactual design however the structure is also the largest. The two n-in-p model show different\n\ndistribution of peaks with the highest one located in the small guard ring structure. The\n\nlarge guard ring structure exhibit more peaks with higher value.\n\nSimulations also have shown that depletion for highly irradiated sensors is an ill de-\n\nfined concept as carrier concentration becomes very low in what is considered to be the\n\nundepleted zone, as seen on figure 3.13 and 3.14. Some low electric field (O(1000 kV/cm))\n\nis present in the simulation in this region that is usually considered undepleted and charge\n\ndeposited in this region could still be collected as its recombination probability is much lo-\n\nwer than in the undepleted regions of an unirradiated sensor. Figure 3.18 show the electric\n\nfield distribution as a function of the depth under a pixel for the ATLAS standard model\n\nirradiated at a fluence of 5× 1015neqcm\n−2. The electric field in the undepleted portion of\n\nthe bulk, from 100 to 300 µm is only an order of magnitude lower than in the undepleted\n\nregion. Since the velocity of carrier saturate and is reduced by an order of magnitude in\n\nthe high electric field present in the depleted region, the drift in each zone would be almost\n\nequivalent.\n\n92\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nFigure 3.18 – Electric field magnitude in the bulk of an ATLAS sensor, under a pixel,\nirradiated to a fluence of 5 × 1015neqcm\n\n−2 and mobility as a function of parallel electric\nfield for electrons\n\nThe simulation of the guard ring structure after irradiation suggests that n-in-p struc-\n\ntures should offer better resistance to irradiation at high fluences as the guard ring structure\n\nwill continue to be effective during all the detector’s operation time. This also suggests that\n\nplacing a guard ring structure on the n side of the n-in-n pixel design could allow operation\n\nafter exposition to the required dose. The small guard ring design endure an higher break-\n\ndown voltage when compared to the large guard rings design. Behavior after irradiation\n\nis however very similar. Smaller guard rings exhibit lower electric field peak in average,\n\nbut the structure simulated is ideal. In reality, smaller guard rings might have higher pro-\n\nbability of defects in process, thus creating weak spot where breakdown could occur. The\n\nhighest peak was also found in the small guard ring model.\n\nTu be used in IBL conditions, the ATLAS standard structure dead region must be\n\nreduced to the level of the n-in-p structures. Simulation have been performed to explore\n\nthe possibility of reducing the dead area of the sensor that span from the edge to the first\n\nguard ring. Simulation for edges ranging from of 100 to 300 µm have been performed.\n\nFigure 3.19 shows the electron concentration for an unirradiated sensor of different dead\n\nedge widths.\n\nWe can observe in these simulation that there is no correlation between guard ring\n\nbehavior and the width of the zone between the outer guard ring and the cutting edge of\n\nthe sensor. The potential and electric field distribution along the guard ring was found to\n\n93\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nbe also the same for each model. After irradiation and SCSI, an undepleted zone remains\n\nvisible even for a small edge width of 100 microns. Reduction of the dead edge outside the\n\nguard ring structure appear to be a valid method to reduce the inactive area of the sensor.\n\nFigure 3.20 show the the majority carrier concentration profile at half depth in the three\n\nmodels under study, unirradiated, as a function of distance from the cutting edge. The\n\nlateral depletion limit can be seen for each model as the distance where the concentration\n\nstart to fall rapidly. This figure shows that the lateral depletion seems to be little influenced\n\nby the type of guard ring structure used in a sensor.\n\nFigure 3.20 – Comparison of lateral depletion for the three model of guard ring structure\nunder study\n\nThe guard rings structure itself also represent an inactive area of the sensor and must\n\nbe reduced, The actual ATLAS pixel sensor has been simulated with 0,1,3,4 of the outer\n\nguard ring removed. Figure 3.21 shows the bias voltage distribution taken by the guard\n\nrings for the different simulated structures with an applied bias of 500 V. It is shown that\n\nthe bias voltage of the guard rings are almost the same as before their removal, with the\n\nouter guard rings moving closer to ground while never reaching it in the case where 6 or\n\n10 guard rings were removed. The rest of the transition occur on the surface of the safety\n\n94\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nedge. Once the guard ring structure get smaller than the lateral depletion zone, it loses its\n\neffectiveness at ensuring a complete transition from high voltage to ground. This limits the\n\nnumber of guard rings that can be safely removed from the actual structure.\n\nFigure 3.21 – Simulated bias voltage distribution for actual ATLAS sensor with 2,4,6 and\n10 of the outer guard rings removed\n\n95\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nFigure 3.22 – Simulated electric field distribution, 0.1 µm under surface, for actual ATLAS\nsensor with 2,4,6 and 10 of the outer guard rings removed\n\nFigure 3.22 shows the electric field distribution at 0.1 µm under the guard rings for the\n\ndifferent simulated structures with an applied bias of 500 V. The high peak next to outer\n\nguard ring is due to the rapid bias voltage drop from outer guard ring to passivated edge as\n\nthe lateral depletion limit pass the outermost guard ring to extend in the outer edge of the\n\nstructure. Removal of 4 guard rings could be use to reduce the width of the actual structure\n\nas the electric field at the outer of remains small compared to the breakdown electric field in\n\nsilicon, valued at 300 kV/cm. If more guard rings are removed , the breakdown probability\n\nis increased by the incapacity of the guard ring to provide a complete transition from edge\n\nto pixel voltage. The peak value at the outer edge of the guard ring can become big enough\n\nto generate a breakdown and electric field present close to the edge can cause excessive\n\nleakage current.\n\nThinning of a pixel sensor can be beneficial to a detector in high fluence environment.\n\nCharge collection occurs in a small region as trapping time becomes smaller than the drift\n\ntime of the charge deposited in the sensor. Charge deposited deeper in the sensor can never\n\nreach the collecting electrode and do no generate signal, leaving most of the sensor useless.\n\n96\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nLower bias voltage is needed and trapping is reduced due to small travel distance of holes\n\nand electrons in the bulk in thinner sensors. I performed simulation of the actual ATLAS\n\npixel design with a thickness of 150, 200 and 250 µm. Figure 3.23 shows the electric field\n\ndistribution 1 µm under the pixels. for a bias voltage of 150 V. We can observe that the peak\n\nelectric field at the guard ring, for the same bias voltage, is not affected by the thickness of\n\nthe sensor. This is because the field distribution is mainly related to the lateral depletion\n\ndepth of the sensor. The lateral dimensions of the sensor are the same so the guard ring\n\nbehave similarly in thinner and thicker sensors. The mean electric field inside the sensor is\n\nhowever higher due to reduced thickness. Lateral depletion at half-height, defined as the\n\ndistance from edge where the silicon is undepleted, is 700 µm for the 150 µm thick model,\n\n540 µm for the 200 µm thick model and 480 µm for the 250 µm thick model.\n\nFigure 3.23 – Electric field 1 µm under the guard rings for different ATLAS pixel sensor\nthickness\n\nFor a given constant bias voltage, we notice that the depletion is more complete in\n\nthe thinner sensor as the electric field is higher. This means that a thin sensor could be\n\noperated at lower voltage, with a guard ring structure behaving like in a thicker sensor.\n\nThe breakdown occurring in guard rings are due to high electric field at the junction edge\n\nof the guard rings. Since this electric field distribution is not dependent on thickness, guard\n\n97\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nring structure are more effective in thinner sensor.\n\n98\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) ATLAS actual design, n-in-n\n\n(b) Large guard rings, n-in-p design\n\n(c) Small guard rings n-in-p design\n\nFigure 3.15 – Simulated backplane current vs bias potential, for different irradiation doses\n\n99\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) ATLAS actual design, n-in-n\n\n(b) Large guard rings, n-in-p design\n\n(c) Small guard rings n-in-p design\n\nFigure 3.16 – Simulated guard rings potential vs bias potential, for different irradiation\ndoses. Bias on, high voltage electrode, pixels at 0V\n\n100\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nFigure 3.17 – Comparison of the electric field at 1 µm under the guard rings for the large\nring and small ring n-in-p model and the ATLAS actual n-in-n model for a bias voltage of\n400V.\n\n101\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) 100 µm edge width\n\n(b) 200 µm edge width\n\n(c) 300 µm edge width\n\nFigure 3.19 – Simulated electron concentration profile for an unirradiated ATLAS n-in-n\npixel sensor for various dead edge width\n\n102\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n3.3.3 The Slim Edge Guard Ring structure\n\nIt was proposed by the ATLAS Planar Pixel Sensor upgrade group to reduce further\n\nthe inactive area of the n-in-n sensor by shifting the pixel under the pixel area, as shown\n\nin figure 3.24. This would reduce the area uncovered by pixel and consequently allow much\n\nsmaller inactive edges.\n\nThis structure was simulated to obtain to understand the effects of this shift on the\n\nperformance of the sensor. The model used was the ATLAS n-in-n standard guard ring\n\nshifted under the pixels by 100,200 and 400 µm. The edge was 500 microns but reduction\n\nof the dead edge does not affect the guard ring behavior and the result can be valid as long\n\nas the width of this edge stay within the 100 microns mentioned earlier. Bias Voltage was\n\n150 V and detector thickness was 300 µm. Figure 3.25 show the electric field magnitude\n\nin the region of the edge pixel where overlap occur, for a shift of 100 and 200 µm. The\n\npresence of the guard ring affect the distribution of the field under the pixel and the field\n\nis weaker in the section of the pixel overlapping the guard ring. Figure 3.26a show the\n\npotential distribution in the 200 µm shift case. We can observe that the potential gradient\n\nin the overlap region will drag charge away from the pixel as the drift closer to it. This\n\nis because this part of the pixel is in the lateral depletion zone that is normally located\n\noutside the volume of the edge pixel. This region is the region where the Ramo potential of\n\nthe edge pixel, shown in figure 3.26b, undergo the largest variation. This will lead to loss\n\nin charge collection. This can still be a beneficial method to reduce the edge of the sensor\n\nwhile keeping larger guard ring structures if the signal collected is still sufficient to obtain\n\na good trigger efficiency.\n\nThe big advantage of the slim edge structure comes after irradiation. After SCSI, the\n\ndepletion occurs from the pixel side and the guard ring become less efficient. The electric\n\nfield distribution at the edge pixel is then changed and the problem of the lateral depletion\n\nzone go away. Figure 3.27 show the electric field configuration in the 200 µm shift model\n\nafter a dose of 1015neqcm\n−2 at a bias voltage of 1000 V.\n\nAs the detector at IBL and SLHC will be during its lifetime irradiated, this strategy is\n\na good compromise to obtain a slim edge structure meeting the requirements of the IBL\n\n103\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\nFigure 3.24 – Slim edge Multi Guard Ring structure showing the overlap of the pixel with\nthe guard rings on the backside\n\nwhile keeping a large Multi-Guard Ring Structure. This guard ring structure was proposed\n\nfor the sensor candidate sensor for the IBL project. The shift is 250 µm in the case of the\n\nso called \"Slim-Edge\" candidate , and 100 µm in the case of the conservative candidate.\n\nTest structure with FE-I3 geometry were produced in the PPSU09 sensor production and\n\nwere used in beam test to study the properties of the edge pixels. Results will be presented\n\nin the next chapter. The simulation made an important prediction on the behavior of\n\nthe slim edge structure that were demonstrated in experiment : The loss of charge at the\n\nedge in the overlap zone between the pixel and guard rings. This phenomenon was later\n\nobserved experimentally. The work done on TCAD simulation of guard ring structure has\n\ncontributed to the design of the various planar pixel structure proposed for the upgrade\n\nprojects. The simulation provided guidelines on the design change that would affect or not\n\nthe performance of the detector. In the next chapter, a comparison of TCAD results with\n\nexperimental data will show the good performance of the simulation in predicting device\n\nbehavior before and after irradiation. Monte-Carlo Simulation of the charge transport was\n\nalso performed to replicate the charge collection behavior observed experimentally.\n\nFor the detector irradiated at super LHC fluence (5− 10× 1015neqcm\n−2), new physical\n\nphenomenon we observed experimentally. In the next section, I present a model that was\n\nelaborated using TCAD simulation to emulate the behavior of highly irradiated sensors.\n\n104\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) 100 µm shift\n\n(b) 200 µm shift\n\nFigure 3.25 – Electric field magnitude in Slim edge Multi-Guard ring structure with a\nshift of 100 and 200 µm\n\n105\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) Potential distribution\n\n(b) Ramo Potential distribution\n\nFigure 3.26 – Potential and Ramo Potential distribution in a Slim edge Multi-Guard Ring\nstructure with a shift of 200 µm. Arrows show the drift direction of electrons\n\n106\n\n\n\n3.3. THE MULTI-GUARD RING STRUCTURE\n\n(a) Potential distribution\n\n(b) Electrifc field distribution\n\nFigure 3.27 – Potential and Electric field distribution in a Slim edge Multi-Guard Ring\nstructure with a shift of 200 µm after an exposition to a fluence of 1015neqcm\n\n−2 . Arrows\nshow the drift direction of electrons\n\n107\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\n3.4 The charge amplification mechanism in highly irradiated\nsilicon sensors\n\nCharge collection efficiency (CCE) of silicon planar pixel sensors used for charged par-\n\nticle detection in high energy physics is known to be reduced by increased exposition to\n\nradiation damage. However it has been observed experimentally [47; 48] that CCE of pla-\n\nnar pixel sensors exposed to fluences of the order of 1015−16 neqcm\n−2 can be increased\n\nby applying higher bias voltage to the sensor. Figure 3.28 show an example of unexpec-\n\nted charge collection in 80 µm pitch, 140 and 300 µm thick n-in-p strip sensors. In this\n\nsection , I present a set of TCAD simulations that have been performed to explore the\n\npossible mechanisms behind this anomalous charge collection observed after exposition to\n\nhigh fluences of diode and strip sensors.\n\nFigure 3.28 – Experimental charge collection in ir-\nradiated thin and thick n-in-p strip sensors showing\nevidence of charge amplification [47]\n\nThe simulations were perfor-\n\nmed on simple geometries to ex-\n\nplore the effects of impact ioniza-\n\ntion and trap-to-band tunneling on\n\nthe transient behaviors of planar\n\nsilicon sensors. A simple one di-\n\nmensional diode geometry was si-\n\nmulated. Implants parameters used\n\nwere obtained from measurements\n\nfrom the PPSU09 production. Two\n\ndimensional simulation of a strip\n\nsensor was also performed but the\n\ncomputing time required to per-\n\nform a transient simulation was too\n\nlarge to obtain a large number of simulation in a reasonable time. For numerical accuracy,\n\nthe 1D simulation considers a width of 250 µm with laterally uniform conditions. The\n\nTCAD simulation have shown poor convergence when aspect ration of the structure is far\n\nfrom unity. DC simulation for a fluence of 2×1016 neqcm\n−2 for a bias voltage up to 3000 V,\n\nwhen numerically possible, was performed for a thick (300 µm) and thin (140 µm) sensor,\n\n108\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\nwith impact ionization switched on and off, then with trap-to-band tunneling switched on\n\nand off. This allows to see the effect of each mechanism on the behavior of the sensors.\n\nTransient simulation of the device was also performed. A 2 ns triangular 1060 nm laser\n\npulse was sent perpendicular to the device surface. The TCAD software then calculates\n\nthe ionization by the laser, mostly uniformly deposited in the bulk depth, and perform\n\na transient simulation over 40 ns at different bias with the trap-to-band tunneling and\n\nthe impact ionization turned on and off. The signal we obtain from the simulation is then\n\nintegrated, with the pedestal subtracted, to obtain the collected charge. From simulation,\n\nwe also obtained the initially deposited charge and we can then compute the CCE using\n\nequation (3.7).\n\nCCE =\nQcollected\nQdeposited\n\n(3.7)\n\nThe DC simulations were performed on a non-irradiated diode, then on a heavily ir-\n\nradiated diode to a fluence of 2 × 1016 neqcm\n−2. Fig. 3.29 shows the current circulating\n\nin the high voltage electrode for a thick and a thin diode with the impact ionization and\n\ntrap-to-band tunneling successively turned off in the simulations. This allows us to see the\n\ncontribution of the different physical phenomena to the DC characteristics of the sensors.\n\nRecombination lifetime was set to 1× 10−5 s for a better simulation of leakage current in\n\nhigh resistivity silicon used for pixel sensors [49].\n\nThis parameter is not taken into account in irradiated diode simulation as it is overrid-\n\nden by the defect introduction model . Fig. 3.29(a) shows the IV curve before irradiation.\n\nA hard breakdown occurs at high voltage with a steep increase in leakage current. The\n\ncontribution to leakage of impact ionization is negligible before breakdown, explaining why\n\nno charge multiplication is observed in non-irradiated sensors. The breakdown occurring in\n\nthe diode is located inside the bulk under the implant, as electric field there becomes too\n\nelevated, as can be seen in the pre-breakdown electric field profile in Fig. 3.30(a), causing\n\nhole multiplication to occurs. Since this is a one dimensional simulation, no implant edge\n\neffects are considered. The contribution of trap-to-band tunneling to leakage current in the\n\ndiode becomes important even for low bias voltage. As electric field increases in the bulk\n\n109\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\nof the sensor , this mechanism contribute to more and more of the leakage leading to this\n\nresistive behavior after depletion. Fig. 3.29(b) shows the IV curve for the same sensors\n\nafter irradiation. The contribution of trap-to-band tunneling to leakage current becomes\n\nsignificant at higher bias voltages than before irradiation. The breakdown occurring in the\n\nsensors before irradiation is replaced by a soft breakdown where the current rises at a much\n\nlower rate even if the same high field region is observed in the thin diode electric field profile\n\nin fig. 3.30(b). This allows the operation of the sensor in this regime where multiplication\n\neffects are to be expected. This quenching of the avalanche mechanism can be explained by\n\nthe increased trapping in irradiated sensors, where the mean free path of holes is reduced.\n\nThis prevents the free holes of creating an avalanche and reduces the contribution from\n\nimpact ionization to leakage current.\n\n110\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\n(a) Current versus bias voltage, non-irradiated 1D diode\n\n(b) Current versus bias voltage, 1D diode φeq = 2× 1016 neqcm\n−2\n\nFigure 3.29 – Comparison of Current versus Voltage curves before and after irradiation.\n\nThe results of the transient simulation can be seen in fig. 3.31. The multiplication\n\neffect can be seen only at high voltage in non-irradiated sensors (fig.3.31(a)), much beyond\n\nthe breakdown voltage. Charge collection follows the normal behavior for non-irradiated\n\nsensors, saturation at a CCE of 1. The multiplication effect in irradiated sensors can be seen\n\nin fig. 3.31(b). Trapping should normally prevent some charge of being collected. A small\n\n111\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\npart of the deposited charge is recovered by trap-to-band tunneling and impact ionization\n\nfurther improves the charge collection efficiency to a factor superior to 1. The electric field\n\nprofile at a bias in the multiplication regime, as seen in fig. 3.30(b) explains the presence of\n\nsuch effect in the irradiated sensor. In this figure is represented the electric field in the bulk\n\nof the diodes at a bias voltage close to breakdown, for non-irradiated sensors, and in the\n\nmultiplication regime for irradiated sensors. A high electric field, of the order of 100 kV/cm,\n\nexists in the bulk of the irradiated sensors leading to de-trapping and multiplication in the\n\nbulk , as seen in fig. 3.31(b). It should be noted that de-trapping alone cannot explain\n\ncharge collection efficency over 1. For the non-irradiated diode, the electric field profile\n\nshown in fig. 3.30(a) shows the pinching if the electric field close to the readout implant\n\nthat eventually cause a hard breakdown , as seen in fig. 3.29(a), at higher bias voltage.\n\nThis breakdown prohibits the operation of non-irradiated diodes at such bias voltage. In\n\ncase of irradiated diodes, the attenuation of impact ionization by trapping allows operation\n\nat higher voltage allowing to reach the multiplication regime.\n\n112\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\n(a) Electric field at bias before breakdown, non-irradiated diodes\n\n(b) Electric field at a bias voltage in the multiplication regime, irradiated\ndiodes φeq = 2× 1016 neqcm\n\n−2\n\nFigure 3.30 – Comparison of Electric field magnitude at a possible operation bias voltage\nbefore and after irradiation.\n\n113\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\n(a) CCE versus bias voltage, unirradiated 1D diode\n\n(b) CCE versus bias voltage, 1D diode φeq = 2× 1016 neqcm\n−2\n\nFigure 3.31 – Comparison of CCE curves before and after irradiation.\n\nThe simulation performed demonstrates that the parametrization of radiation damage\n\nin terms of effective defects introduction, combined with trap-to-band tunneling and im-\n\npact ionization qualitatively explains the charge recovery effect observed experimentally\n\nin highly irradiated n-in-p diodes. The high electric field present at the readout implant\n\ncausing multiplication of free charges, combined with increased de-trapping caused by the\n\n114\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\nbending of the band gap structure by the electric field and the attenuation of the multipli-\n\ncation by increased trapping provides a physical explanation to the observed behavior of\n\nthese diodes.\n\n115\n\n\n\n3.4. THE CHARGE AMPLIFICATION MECHANISM IN HIGHLY IRRADIATED\nSILICON SENSORS\n\n116\n\n\n\nChapitre 4\n\nFrom TCAD simulation to\nexperimental data\n\nTCAD simulation models presented in the last chapter require the input of a large\n\nnumber of parameters to obtain quantitatively comparable results. Process parameters de-\n\ntermine the shape and concentration profile of the implants forming the guard rings and\n\nthe readout electrodes. Quality of the silicon dioxide and silicon interface is represented\n\nby the surface charge used. The resistivity of the bulk and recombination lifetime of the\n\ndefects present in the bulk affect depletion potential and leakage current magnitude. Ex-\n\nperimentation on test structure and sensors can help to obtain the parameters needed to\n\ntune the simulation models and obtain quantitative results.\n\nAccurate TCAD simulation can offer better understanding of the behavior of the AT-\n\nLAS existing and future pixel sensors. The inclusion of radiation damage behavior and\n\nedge effects using knowledge from TCAD simulation in the digitization of pixel sensors\n\nused in the ATHENA simulation of the ATLAS detector can improve the realism of the\n\nsimulation and help understand effects observed during the lifetime of the inner detector.\n\nA Monte-Carlo charge transport code was built to study transient behavior of sensor using\n\nTCAD electric field and Ramo potential simulation as input and provide more information\n\non sensor detection properties. As mentioned in the previous chapter, a sensor production\n\nincluding designed structure influenced by our TCAD simulation presented in this work\n\nwas delivered to us in 2010. This production included many test structures to study our\n\nTCAD models. In this chapter, I present the experimental work that was performed to\n\n117\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\ncalibrate the simulation models used in this work. Part of the work behind this has been to\n\nassemble test bench for low noise,low current and high voltage DC and transient characte-\n\nrization of silicon sensors. Clean room and test beam measurements were used to calibrate\n\nthe parameters used in simulation and get more knowledge on the physics describing guard\n\nrings structure.\n\nThe PPSU09 production contained ATLAS pixel structure with FE-I3 geometry with\n\nmany variations of slim edges structure and reduced number of guard rings. These sen-\n\nsors were bonded to FE-I3 chip and tested before and after irradiation with high energy\n\nprotons and neutrons in test beam at CERN SPS and DESY electron synchrotron. The\n\nEUDET telescope was used to provide track information used to analyze the pixel sensor\n\ndata. Results from reconstruction and analysis of these sensor have been used to validate\n\npredictions done using TCAD simulation. Finally , a digitization model for the FE-I3 and\n\nFE-I4 planar pixel sensors candidate for IBL was developed using knowledge gathered from\n\nGEANT4 Monte-Carlo and TCAD simulation. The GEANT4 simulation of the EUDET\n\ntelescope setup was used to simulate test beam conditions including digitization method\n\nand was used to validate the model. Comparison of test beam data and simulation data\n\nshow good agreements and reproduce well edge and radiation damage effects that will\n\noccurin the ATLAS detector after long operation time.\n\n4.1 Experimental validation of TCAD simulation\n\nThe PPSU09 production contained a set of test structures that we included for to cali-\n\nbration of TCAD simulation models. Table 4.1 show the structure and their experimental\n\npurpose and figure ?? show the layout of the wafer for the n-in-n production.\n\n118\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nFigure 4.1 – N-in-N PPSU09 production wafer\n\n(a) doping profile structure (b) Inter-pixel capacitance\nstructure\n\n(c) LAL diodes\n\nFigure 4.2 – Test structure inserted in the PPSU09 production\n\nThe main goal of the production was to produce prototype of sensors with reduced edge\n\nfor the IBL ATLAS pixel sensor. Several FEI3 pixel structure with guard ring removed were\n\ninserted in the production. The main detector occupying most of the wafer is a FEI4 sensor.\n\nThe n-in-p production contained :\n\n– 3 FE-I3 standard small guard ring design , p-spray\n\n– 3 FE-I3 standard small guard ring design, moderated p-spray\n\n– 3 FE-I3 small guard ring design, 8 guard rings, p-spray\n\n– 3 FE-I3 small guard ring design, 15 guard rings, p-spray\n\n119\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nTable 4.1 – Test structure inserted in PPSU09 production\n\nStructure Description Purpose\nDoping profile\nstructure\n\nThree 1.9 mm × 8.9 mm\nzones with n+, p-spray\nand moderated p-spray\nimplants on front side and\np+ implant on backside.\nFigure 4.2a\n\nAtomic Force Microscopy,\nSpreading Resistance Pro-\nfiling (SRP) and Secon-\ndary Ion Mass Spectro-\nscopy (SIMS).\n\nInter-Pixel Capa-\ncitance structure\n\n5×5 pixel matrix surroun-\nded by a standard guard\nring structure. First neigh-\nbors of the central pixel\nare connected together and\nindependently the second\nneighbor also. Pixel size of\n50, 250 and 400 µm have\nbeen used. Figure 4.2b\n\nInter-pixel capacitance\nmeasurement in clean\nroom.\n\nLAL diodes 6× 6 mm diodes with 1 to\n4 large contactable guard\nrings. Figure 4.2c\n\nLeakage current and guard\nring potential measure-\nment before and after irra-\ndiation.\n\n– 1 FE-I4 small guard ring design, p-spray\n\n– 1FE-I4 small guard ring design, moderated p-spray\n\nThe n-in-n production contained :\n\n– FE-I3 ATLAS standard guard ring with 3,5,11 and 13 outer guard ring removed\n\n– FE-I3 ATLAS standard guard ring with pixel shifted stepwise by 50 microns in 8\n\nsteps\n\n– FE-I3 ATLAS standard guard ring with pixel shifted by 100 and 200 µm\n\n– FE-I4 4 chip module with ATLAS standard guard ring\n\n– FE-I4 ATLAS standard guard ring\n\nA second n-in-n production was submitted to produce IBL planar sensors. Two FE-\n\nI4 sensor were used in this production. The conservative model has the ATLAS standard\n\nguard ring structure with the 3 outer guard ring removed, as suggested in last chapter. The\n\nSlim Edges design has also reduced guard ring, but pixel are also shifted 250 microns under\n\nthe guard rings. The measurement presented in the next section were performed using the\n\n120\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nstructure from these productions.\n\n4.1.1 Doping profile measurements\n\nFigure 4.3 – Spreading Resistance Profiling\ntechnique\n\nTCAD simulation results such as guard\n\nring potential and breakdown voltage are\n\ndependent on the doping profile of the struc-\n\nture. Knowing the doping profile of the dif-\n\nferent implant is therefore an important step\n\nto validate the simulation model and obtain\n\naccurate simulation. Various method exist\n\nto obtain the dopant concentration profile of\n\nan implant. The method can be divided in\n\nthe stoichiometric and the electric methods.\n\nThe stoichiometric methods consist in mea-\n\nsuring the total amount of dopant in the si-\n\nlicon bulk. One method used is Secondary\n\nIon Mass Spectroscopy. It consists in sputtering the surface of a sample using an ion beam\n\nand analyze the ejected secondary ions using a mass spectrometer. The speed at which the\n\nion beam dig in the sample is known and the amount of ions of each species measured in\n\nthe spectrometer as a function of time can be converted into a concentration versus depth\n\nprofile. The SIMS doping profile were performed at CNRS-Meudon.\n\nTwo electrical methods were also used to characterize the implants. Electrical methods\n\nmeasure the carrier concentration in the implant, which is related to the electrically active\n\ndopant concentration. The first method used was Spreading Resistance profiling. This\n\nmethod consist in probing the local resistance between two very close point on the implant\n\nusing small tungsten needles. The implanted structure is beveled with a small angle to\n\nreveal the implant at different depth. The needle probing is done at different points along\n\nthe bevel to measure the local resistance between the two needle. The resistivity (ρ) of\n\nsilicon vary with active dopant concentration hence the measure of the Resistance R can\n\nbe converted into a active dopant profile. The distance from the surface on the beveled\n\n121\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nplane can be converted to depth of the implant by multiplying the distance by the sinus of\n\nthe bevel angle. Figure 4.3 show the principle of the measurement. The SRP measurements\n\nwere performed by Evans Analytical Group. The second method that was investigated and\n\ndeveloped was the Atomic Force Microscope Spreading Resistance Profiling (AFMSRP).\n\nThis method is analogous to SRP done with the cantilever of an atomic for microscope\n\nthat is electrically connected to an electrometer.\n\nTable 4.2 – Comparison of SIMS and SRP method\n\nMethod SRP SIMS\nResolution 2% 5%\n\nSensibility (cm−3) 1012 − 1020 1015 − 1020\n\nSample size several mm2 >1 mm2\n\nThe measurement were performed on the doping profile structure of the PPSU09 n-\n\nin-n and n-in-p production. This structure was designed to allow many kind of doping\n\nprofile measurement methods on the same test structure. Three zone representing the\n\nreadout n+ implant and the two p+ implant, one representing the p-spray implant and\n\nthe other representing the non-moderated part of the moderated p-spray. The backside of\n\nthe structure is covered by a p+ implant.\n\n4.1.1.1 n-in-n production\n\nTwo samples , labeled 02-42 and 05-42, were tested using both SRP and SIMS method.\n\nfigure 4.4 show to profile that were obtained from the SRP and SIMS method. The n+\n\nimplant corresponding to the pixels of the n-in-n sensors is shown in figure 4.4a. Measure-\n\nment on both sample show the same profile showing a good uniformity of the phosphorus\n\nimplantation on the different wafers of the production. Bulk effective dopant concentration\n\nof the sample can be determined from the concentration of active dopant far from the junc-\n\ntion in the SRP measurement. For the n-in-n wafer sample tested, Neff = 4.15×1012cm−3\n\nfor sample 05-42 and Neff = 1.15×1012cm−3. Using equation 2.5 and 2.6, we can compute\n\nthe expected depletion potential of a 285 µm sensor and compute its resistivity. Sample\n\n02-42 has a resistivity of 4 kΩcm−1 and a depletion voltage of 80V while sample 05-45 has\n\n122\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\na resistivity of 2.2 kΩcm−1 and a depletion voltage of 291V. The profile might however not\n\nhave been performed deep enough to obtain bulk resistivity.\n\nThe p+ implant measurements, corresponding to the backside and guard ring implants\n\nin the n-in-n sensors, are shown in figure 4.4b. The pn junction is clearly visible by the\n\ndrop in active dopant effective concentration as the silicon transition from p to n type. The\n\ndepleted region of the junction is characterized by a low concentration as p and n type\n\ndopant compensate each other leading to a quasi null effective dopant concentration and\n\nvery high resistivity. junction can be found at 582 nm for sample 02-42 and at 676 nm for\n\nsample 05-42. An order of magnitude is seen between the activated and total concentration\n\nof boron present in the implant possibly pointing to and incomplete activation of the\n\nimplant or a compensating defect present in the bulk lowering the effective active dopant\n\nconcentration. The effective active dopant for sample 02-42 in this measurement is Neff =\n\n1.15 × 1012cm−3 for sample 02-42 and Neff = 1.85 × 1013cm−3 for sample 05-42. This\n\nyield to a resistivity of 248.4 Ωcm−1 and an unrealistic depletion voltage of 1300 V. This\n\nmean that doping profile was not performed deep enough to obtain bulk resistivity which\n\nis expected to be over 1000 Ωcm−1. For sample 05-42, we obtain a bulk concentration of\n\nNeff = 1.15× 1012cm−3 leading to a a resistivity of 4 kΩcm−1 and a depletion voltage of\n\n80V, compatible with sample 02-42 for the n implant measurement.\n\n123\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) N+ implant\n\n(b) P+ implant\n\nFigure 4.4 – N+ and P+ Doping profiles measurement on the n-in-n samples of the\nPPSU09 production\n\nP-spray and moderated p-spray implantation profiles were also measured on these test\n\nstructure. The moderated p-spray correspond to a shallow low concentration boron implant\n\nwhile the p-spray correspond to a deeper implant with slightly higher concentration. The\n\ndifference between both profiles, originationg from the same implantation of boron on the\n\nfrontside of the wafer is the nitride layer that is used as a filter that is present for the\n\ncase of low p-spray implant. Figure 4.5a and 4.5b show the two implant profiles measured\n\nby the SRP and SIMS method. SIMS measurement are uniform between sample and the\n\n124\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\npeak concentration of the p-spray implant is shifted with regard to the moderated p-\n\nspray implant as expected because of the presence of the additional nitride layer during\n\nimplantation. SRP measurement, however, show no sign of a junction for all measurements\n\nexcept for the p-spray of sample 02-42. As boron presence is confirmed by SIMS, it is highly\n\nprobable that the activation step of the p-spray implants was not completed correctly\n\nduring the processing of the diodes . No p-spray was used in the simulation of the n-in-n\n\nsensor although it was implemented as a possible step in the simulation template.\n\n4.1.1.2 n-in-p samples\n\nThe same measurements shown in last section were performed on a doping profile test\n\nstructure and a standard diode of the n-in-p PPSU09 production from the same wafer.\n\nFigure 4.6a show the n implant representing the pixel and guard rings implants of the\n\nn-in-p pixel sensors. The SIMS profile confirm the presence of a phosphorous layer in the\n\nimplant and the measurement are compatible with the measurement performed on the\n\ndiode’s n implant. SRP measurement show that activating of the implant was completely\n\ncorrectly. However, no junction is found in the profile. A junction is however found on\n\nthe backside of the sample as show in figure 4.6b. As the bulk is expected to be p-type,\n\nposition of the junction is incorrectly located in the device. This lead us to suspect the\n\npresence of an additional donor in the bulk of the sensor that inverts the type of the bulk\n\nin the manner of radiation damage. It was found that the wafers from were originated\n\nthese sample was subjected to an oxygenation at high temperature to increase its radiation\n\nhardness. The heating of the oxygenated silicon at temperature between 300 and 550 C\n\nwill lead to the formation of thermal donors [50]. While thermal donor are annealed at\n\nhigher temperature used for oxidation, if cooling of the wafer back to to room temperature\n\nis not done sufficiently fast, formation of thermal donors could have occurred during the\n\ntransition. These thermal donor can eventually be sufficiently abundant to invert the type\n\nof the silicon bulk. Further SRP measurements were performed on the diode structure\n\nto determine how deep in the bulk the substrate was inverted. Figure 4.8 show the SRP\n\nmeasurements made on the sample diode on the first 15 microns of the implant. The\n\nmeasurements were performed down to 140 µm under the p and n implants. A junction\n\n125\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) moderated p-spray implant\n\n(b) p-spray implant\n\nFigure 4.5 – Insulation doping profiles measurement on the n-in-n samples of the PPSU09\nproduction\n\n126\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nwas found on the backside but in not shown in the figure as stepping was too large. The\n\neffective dopant concentration found at the center of the diode was Neff = 2.6×1011cm−3,\n\ncorresponding to a n-type resistivity of 17.6 kΩcm−1 and a depletion voltage of 18 V\n\nconsidering a 300 µm thick sensor.\n\n(a) N+ implant\n\n(b) P+ implant\n\nFigure 4.6 – N+ and P+ Doping profiles measurement on the n-in-p samples of the\nPPSU09 production\n\nThe p-spray and moderated p-spray implantation are extremely important for the n-in-\n\np sensors as the guard ring structure is located on the electron collection surface. Figure 4.7\n\nshow the doping profile measure on the samples for these implants. The SIMS measurement\n\n127\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nconfirm the presence of boron and the SRP measurement show a well activated profile. The\n\nmoderated p-spray profile show a shift towards the left with regard to the p-spray profile,\n\nas expected. A junction can be found in both SRP profile confirming again the inversion\n\nof the bulk type due to thermal donor generation.\n\n(a) moderated p-spray implant\n\n(b) p-spray implant\n\nFigure 4.7 – Insulation doping profiles measurement on the n-in-p samples of the PPSU09\nproduction\n\nNew sensor production taking into account the results from our measurement were\n\nplanned to fix the problems encounter in the process. Independent measurements using\n\n128\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nFigure 4.8 – SRP measurement performed deep into a n-in-p diode sample\n\nSIMS was performed by LPHNE laboratory and confirm the results presented here. While\n\nSIMS and SRP represent efficient methods to characterize the implants of a production, the\n\ncost of each measurements limits the quantity that can be performed for each production.\n\nin the next section, I present a method that is in development at LAL to perform electrical\n\nprofile measurement using our own setup.\n\n4.1.1.3 Atomic Force Microscopy Spreading Resistance Profiling\n\nAtomic Force Microscopy (AFM) measure the repulsive and attractive forces between\n\na nanometer scale cantilever and the surface atoms of a sample. Figure 4.9 show the sche-\n\nmatics of a typical AFM measurement. The cantilever tip’s distance from the surface is\n\nmeasured using the deflection of a laser ray reflecting on its back surface. A very accurate\n\npiezoelectric translation system is used to scan sample surface with the cantilever and ob-\n\ntain a measurement of the topology of a surface. This method allow to study nanoscale\n\nfeature of a surface and allow to perform accurate measurements while scanning on short\n\ndistances. The Institut d’Electronique Fondamentale (IEF) located on Orsay campus is\n\nequipped with an AFM equipped with a resiscope. The resiscope is used to measure the\n\nelectrical resistance between the tip of the needle and the backside of a sample when a\n\ncertain bias voltage is applied. This apparatus allows us to scan and measure local resisti-\n\n129\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nvity of beveled implant structures by scanning the surface of the bevel and measuring local\n\nresistivity. Calibration sample with known resistivity can be used to calibrate the method\n\nand translate resistance measurements into carrier concentration profiles.\n\nFigure 4.9 – Atomic force micro-\nscope schematics\n\nIn order to proceed with the measurement, a be-\n\nvel of a small known angle had to be etched from the\n\ndoping profile test structure. IEF own a polishing ma-\n\nchine,a MECAPOL P400, that can be used to etch\n\nand polish the surface of our sample. A mechanical\n\nstructure was design by Tristan Vandenberghe, me-\n\nchanical engineer at LAL, to allow to polish the small\n\nsilicon test structure. Figure 4.10 show the CAD dra-\n\nwing of the polishing device.\n\nFigure 4.10 – Sample holder designed for small angle beveling in doping test structure\nusing the MECAPOL P400 polishing machine.\n\nFigure 4.10 show a bottom view of the device\n\nshowing the sample holder with a doping profile test\n\nstructure fixed on the angle surface using double si-\n\nded conductive tape. The beveling of the surface is performed by first removing the extra\n\n130\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nlayer of silicon from the sample using a 2400 grain per square inch abrasive paper. The\n\nsurface of the bevel is then polished using successively small grain polishing liquid with\n\ngrain size of 9,3 and 1 µm. A final polishing is done using a commercial colloidal silica\n\nsolution named NALCO. Through the polishing process, the rugosity of the surface has\n\nbeen monitored using a profilometer throughout each step to adjust the step of the poli-\n\nshing protocol to develop a standardized procedure. Optical inspection of the sample has\n\nalso been performed after each polishment. Figure 4.11 show the evolution of the typical\n\nprofile of the surface after various step of the process. It can be observed that the variation\n\nin height of the surface is reduced after each polishing step. The final state of the surface\n\nshould exhibit defects of a typical size of 1 µm or less. The final angle of the beveled\n\nstructure was measured by mechanical profilometry to allow for correct translation from\n\nmeasured distance from bevel edge to doping profile depth.\n\nFigure 4.11 – Evolution of rugosity of a doping profile test structure after successive\npolishing steps\n\nAt the moment of writing these lines, several problem have been observed in the po-\n\nlishing procedure. The problems range from wrong polishing time to incompatibility of\n\ncertain substance present at the surface of certain sample, such as the BCB used to insu-\n\nlate the guard rings of the n-in-p sensor, with the polishing liquids. However, preliminary\n\n131\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nresults obtained on two samples shown the feasibility of the method and its ability to\n\nmeasure small implant structure. Raw results of measurements performed on the n-in-p\n\ntest structure are shown in figure 4.12. The results show the resistance of the surface as a\n\nfunction of the position of the scanning cantilever. The resistance is expressed in terms of\n\nthe raw output of the resiscope in V, ranging from 0 to 10 V corresponding to a resistance\n\nrange of 102 to 1012 Ω. Calibration sample will be measured to obtain an absolute value of\n\nthe carrier concentration. Knowing the bevel angle, measured with mechanical profilome-\n\ntry, we can obtain the actual doping profile of the implant by multiplying the x distance\n\nby the sinus of the bevel angle. The relative depth of the implant is in agreement with the\n\npreviously measured value for this structure.\n\n(a) n+ implant (b) moderated p-spray implant (c) p-spray implant\n\nFigure 4.12 – Raw image of the resiscope signal for a scan of a 50x50 µm surface. Right\nside of the figure represent the surface of the structure.\n\nFigure 4.13 show the converted doping profile of the n implant of figure 4.12, averaged in\n\nthe X direction. The calibration between resistance and concentration values was done using\n\npreliminary measurements done on calibration sample with fixed carrier concentration. The\n\nvery good agreement between the preliminary results obtained and the other doping profile\n\nmeasurement gives us confidence in the reliability of the method and further step will be\n\ntaken to standardize the method to obtain reproducible results.\n\nAnother advantage of the AFM method is that it allows us to get two dimensional image\n\nof the implants if the implant lateral dimension can be constrained within the scanning\n\nwindow of the AFM, usually 50 x 50 µm. Trials have been performed to measure the p+\n\nimplant of the ATLAS n-in-n guard ring structure. A FE-I3 sensor was cut to expose the\n\nguard ring structure. The surface of the guard rings was beveled to expose the implants\n\n132\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nFigure 4.13 – Comparison between the doping profile of the n implant of the n-in-p\nproduction with the SRP and SIMS measurements performed on the sample\n\nand an AFM measurement was performed on the sample. Figure 4.14 show the converted\n\nprofile obtained on the sample. The surface of the sample, covered by oxide and nitride, can\n\nbe seen in the lower part of the plot where resiscope output values are very high. This high\n\nresistance is due to the bad contact between the cantilever and the oxide. Two complete\n\nguard ring implants of a 10 µm width can be seen, as expected for this structure. The\n\ndepth of the implants is compatible with the measured depth for the n-in-p production.\n\nFurther work is needed to obtain more accurate and less noisy measurement. However, the\n\npreliminary results obtained and presented here have demonstrated the feasibility of the\n\nmethod. No other doping profile measurement methods allow for two dimensional profiling\n\nof the implants. This method makes it a very interesting candidate for comparison with\n\nTCAD simulation model of implantation required to create the structure used in electrical\n\nsimulation.\n\n4.1.1.4 Calibration of TCAD implantation model with experimental data\n\nWith the data of the PPSU09 doping profile structures, we were able to determine\n\nsome of the main missing parameters needed for accurate simulation of the implantation in\n\n133\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) SPR Raw measurement\n\n(b) Photography of the measured region\n\nFigure 4.14 – 2D doping profile of an n-in-n ATLAS standard guard ring structure\n\n134\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nTCAD process simulation. As p-spray and moderated p-spray were shown to have problems\n\nin the production, the experimental profile were used directly in the simulation to avoid\n\nthe complex problem of finding the process step leading to such profile. Same method was\n\ndone for simulation of the n-in-p sensor’s n and p implant. The presence of thermal donor\n\naffect the doping profile and makes it very hard to disentangle the different process step\n\nleading to such profile .\n\nApart from the lack of activation of the p-spray for some sample, the n-in-n sample have\n\nshown the expected doping profile characteristics. The parameters needed to reproduce the\n\nimplantation process of these implants are the temperature and duration of the thermal\n\nannealing used to activate the dopants, the thickness of the oxide layer used a screen and\n\nthe energy and dose of the implanted ions. The Modified Levenberg-Marquart algorithm\n\n[39], built-in SILVACO software, was used to obtain the value of these parameters that\n\nproduce a simulation of the doping profile that most closely fits the experimental SIMS\n\nand SRP measurements.\n\nTwo models are available in SILVACO TCAD software to simulate the diffusion of the\n\ndopants : The fermi diffusion model and the fully coupled diffusion model . The fully coupled\n\nmodel includes the physics of the previous model and adds a new phenomenon needed for\n\nmore precise simulation of small details of the doping profile. The physics behind each of\n\nthe model is described in the TCAD software manual [39]. Two implantation models are\n\nalso available. A simple model which consist of a parametrization of implant profiles using\n\nvarious SIMS measurements can be used for fast simulation. A Monte-Carlo model using\n\nthe binary collision approximation, can be used to obtain a more accurate description of\n\nthe implant. However, the computing time needed increases by an order of magnitude when\n\nusing the more accurate model. This limits the usability of the two last models for large\n\nstructure simulation.\n\nOptimization of the implantation parameters was performed using the simple diffusion\n\nand implantation models. The advanced models were then activated to compare the ob-\n\ntained profile to the SIMS measurements. figure 4.15 show the comparison between the\n\ndoping profile obtained and the SIMS profile for the n-in-n p and n implants. It can be\n\nobserved that the simulated profile reproduce well the shape of the experimental data. As\n\n135\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nmore complex model are used, even more accurate description of the profile is obtained.\n\nParameters used to produce these profiles are shown in table 4.3. These parameters we\n\nused for the n-in-n sensor simulation shown in the last chapter. Experimental profiles were\n\ndirectly injected in the simulation in the case of the p-spray implants. Simulation of the\n\nn-in-p structure were also performed using the experimental data when available.\n\nTable 4.3 – Optimized doping and diffusion parameters for the n-in-n PPSU09 production\n\nParameter N+ implant P+ implant\nOxide thickness (nm) 40 40\n\nImplantation dose (cm−3) 1.38e15 5.5e14\nImplantation energy (keV) 10 10\nAnnealing temperature (C) 1350 1293\nAnnealing duration (min) 0.22 0.15\n\n4.1.2 Guard Ring measurements\n\nThe guard ring structure of the planar pixel sensor of the PPSU09 production have been\n\nsimulated to predict their behavior under bias. The main measurable parameter is the bias\n\nvoltage taken by the guard ring when the sensor is biased. In the clean room, a setup\n\nusing a probe station enclosed in a copper faraday cage, connected to a Keithley 6517B\n\nhigh voltage source and an electrometer was used to measure the potential distribution\n\nof various guard ring structures. An electrometer with a high input impedance (1020 Ω)\n\nwas used to ensure that the measurement of the guard ring voltage does not influence the\n\nsensor and modify the results. As guard rings are floating structure unconnected to any\n\nbias source, using a standard voltmeter with lower impedance would draw current from\n\nthe guard ring modifying its electrical potential . N-in-p sensor were simulated using an\n\nn-bulk as it has been observed in the last section that the bulk type has been inverted by\n\nthermal donors.\n\nThe first measurements were performed on the three guard ring structure presented in\n\nfigure 3.9. Figure 4.16 show the simulated and measured guard ring potential for the three\n\nguard ring models. The results obtained are in good agreement with the experimental data,\n\ngiving us confidence that the TCAD simulation model correctly represent the physical be-\n\n136\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) N implant\n\n(b) P implant\n\nFigure 4.15 – Comparison of simulated and measured n and p doping profile of the\nPPSU09 production after optimization of implantation and diffusion parameters\n\n137\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) ATLAS standard structure\n\n(b) Small guard ring structure\n\n(c) Large guard ring structure\n\nFigure 4.16 – Comparison of simulated and measured guard ring potential of the PPSU09\nproduction guard ring structure after optimization of implantation and diffusion parameters\n\n138\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nhavior of multi-guard ring structures. Some discrepancies that could not be easily explained\n\ncan be seen in the outer guard rings of the small guard ring n-in-p structure. The mea-\n\nsurement of these guard rings have shown poor reproducibility hinting at some transient\n\nphenomenon. A possible explanation could be an oscillation of the guard rings around the\n\nedge potential due to some unknown effect that wasn’t reproduced in simulation. The use\n\nof the experimental doping profile data has been very important to obtain correct results.\n\nIt has been observed that the guard ring’s bias potential obtained in simulation was very\n\ndependent on the doping profile characteristics of the n, p and p-spray implants. The use\n\nof the profile measurement allowed us to reduce the number of free parameters needed for\n\nthe simulation and resulted in the correct guard ring behavior.\n\nThe size of the guard rings of the three model studied here doesn’t allow easy measure-\n\nments. Contact with the guard ring has to be done by etching mechanically the passivation\n\nlayer over the guard ring structure using the probe station needle, which can eventually\n\nreduce the performance of the sensor. A limited number of full guard ring structure were\n\nmeasured to keep its integrity for future use for irradiation or beam test studies. As men-\n\ntioned in the first section of this chapter, a set of test diodes with large contactable guard\n\nrings was placed in both the PPSU09 n-in-n and n-in-p production. The number of guard\n\nring on the diode span from 1 to 4, the same guard ring size for each structure. For example,\n\nthe 4 guard ring model contained the 3 guard rings of the 3 guard ring diode plus a fourth\n\none added on the outer edge of the structure. The implant of the guard ring structure are\n\n60 µm wide, with the electrode width being respectively, from the inner to outer guard\n\nring, 120, 150,180 and 210 µm. The electrode overshoot the implant toward the outer edge\n\nof the diode by 18 µm and the distance between each ring is 10 µm. Measurement of the\n\nguard ring potential of the structure was done for the diodes of the PPSU09 n and p bulk\n\nproduction. Measurements within each production have shown a variation of less than 10\n\n% between the measured guard ring potential for the diodes coming from different wa-\n\nfer. A good reproducibility has been observed as the same structure have led to the same\n\nresults when measured two months after the first measurements. Two set of four diodes\n\nn-in-n diodes have been irradiated at CERN with 24 GeV protons to 1× 1015neqcm\n−1 and\n\n4×1015neqcm\n−1 . A set of n-in-p diode was also irradiated to a fluence of 2×1015neqcm\n\n−1.\n\n139\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nThe sample were kept under 0 C after irradiation during transport and were kept at -28C\n\nduring storage to avoid annealing effects.\n\nA TCAD simulation of the four diodes was performed for each type of bulk, before and\n\nafter irradiation. Figure 4.17 show the comparison of the simulated and measured guard\n\nring potential for four diodes of the n-in-p production. The removal of the outer guard\n\nrings made no change to the guard ring potential of the inner guard rings and only results\n\nof the 4 guard ring simulation are shown in this figure. This is analog to the simulation\n\nof the removal of the ATLAS standard guard ring structure similar to what described in\n\nchapter 3. In both case, the removal of the outer guard ring have only minor influence\n\non the potential taken by the other guard rings. Simulation reproduce well the guard ring\n\npotential curves at lower bias voltage but fails at higher voltage. However, the general\n\nbehavior of the guard ring structure is well reproduced by the simulation. Other guard\n\nring measurements results have shown better agreement with simulation The discrepancy\n\nbetween simulation and measurements could be induced by a physical phenomenon not\n\nincluded in the simulation or systematic effect in the measurements when measuring large\n\nguard ring as it is the case here.\n\nThe n-in-n diode simulation have shown the same results as for the n-in-p diode si-\n\nmulation. The guard ring behavior seems to be independent of the substrate type. The\n\nguard ring structure have been measured on the n-in-p and n-in-n irradiated diodes. Fi-\n\ngure 4.18 show the measured potential on 2 of the diodes irradiated to a dose of less than\n\n2×1015neqcm\n−1]. The guard ring seems, in both cases , to completely stop working. In the\n\ncase of the n-in-n structure, this was predicted to happen after irradiation. For the n-in-p\n\nstructure, the simulation predicts working guard rings. However, it was discovered that\n\nincreasing the surface charge up to a value of 2× 1012cm2 produce the observed effect. In\n\nthe simulation, the guard ring stay shorted to ground potential for any bias voltage. The\n\nvariation seen in the experimental data are not reproduced in details but the very low value\n\nand the uniformity of the potential on the guard rings show that they are not functional\n\nany more and the value measured is probably the result of a systematic effect present in\n\nthe measurement setup.\n\nThe p-spray insulation used and measured in the n-in-p production might not be suffi-\n\n140\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nFigure 4.17 – Comparison of measurement and TCAD simulation of the potential of the\nLAL diode test structure for the n-in-p production\n\ncient to ensure proper insulation of the guard rings after irradiation with charged particles.\n\nNeutron irradiation of these diode structure should be performed to evaluate the decouple\n\nthe measurement and pinpoint the different effect affecting the guard ring. P-spray si-\n\nmulation seems to overestimate the insulation power of the implant and further work is\n\nneeded.\n\n4.1.3 Current versus Bias characteristics\n\nAn important parameter of pixel sensor are its leakage current and breakdown voltage.\n\nSimulation of leakage current represent a difficult task as all simulation are performed in\n\ntwo dimension, on sub-volume of the actual detector. Breakdown voltage prediction can\n\nhowever be done from simulation and compared with experimental results. Leakage current\n\nwas measured in the various FE-I3 and FE-I4 structure of the n-in-n and n-in-p produc-\n\ntion. Figure 4.19 show the results of these measurements for the FE-I3 sensors. The n-in-p\n\nproduction contained two types of sensor using respectively the p-spray and the modera-\n\nted p-spray insulation method. It can be clearly observed that p-spray sensors have lower\n\n141\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) n-in-n,1× 1015neqcm\n−1\n\n(b) n-in-p,2× 1015neqcm\n−1\n\nFigure 4.18 – Measured guard ring potential for LAL diodes after irradiation\n\n142\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nbreakdown voltage, between 200 and 250V, as the p-spray implants surrounding the n im-\n\nplants generate high electric field region where high leakage current can be generated. The\n\nbreakdown observed in these sensor is not a steep brutal one but rather a slow exponen-\n\ntially raising leakage current. The moderated p-spray sensors show breakdown around 400\n\nV. In moderated p-spray, the p implant surrounding the n implants is less deep than the\n\np-spray implant and this has for effect of reducing the electric field and leakage current\n\nin these regions. Simulation predict a much higher breakdown voltage for these sensors,\n\naround 1000V. It is however known that other sensor of the same production show higher\n\nbreakdown voltage. The wafer used for these measurement was discarded and considered\n\nas a bad wafer in view of its lower breakdown voltages. The wafer is however useful for\n\nsensor characterization and irradiation studies. FE-I4 sensors with p-spray and moderated\n\np-spray were also present on this wafer and leakage current measurement are shown in\n\nfigure 4.20. Again in this case the moderated model show better breakdown voltage but\n\nstatistics was too low to draw any conclusion.\n\nThe guard ring diode were also extensively measured before and after irradiation. At\n\nthe moment of writing these line, no cooled probe station chuck were available to perform\n\nmeasurements on the irradiated diodes at a lower temperature to limit leakage current.\n\nThe results shown here are of limited interest but still show the increase of leakage current\n\nwith exposed fluence. Figure 4.21 show the IV characteristics of the diodes that were\n\navailable for measurements. An interesting behavior can be observed in the case of the n-\n\nin-p diodes. The leakage current curves shown here for these sensors were taken successively\n\non the same diode during overnight runs. The breakdown voltage observed in the curves\n\nfor the n-in-p diodes drifted during the night getting higher until it settles to a stable\n\nhigher value. It can be interpreted as a reorganization of the surface charge within the\n\noxide layer due to the added presence of an electric field. Local hotspot breakdown due to\n\nlocally accumulated charge are diluted as the charge drift in the field present in the oxide,\n\nrevealing the real breakdown value of the diode. The big variation in breakdown from a\n\ndiode to another, does not allow to draw conclusion on the relation between the number of\n\nguard ring and the breakdown voltage of the diodes. The n-in-n diode measurement show\n\nthe only measurement available for n-in-n diode at the moment of writing these line. Most\n\n143\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) n-in-n production\n\n(b) n-in-p production\n\nFigure 4.19 – Leakage current in the FEI3 of the PPSU09 production\n\n144\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nFigure 4.20 – n-in-p small guard rings FE-I4 leakage current\n\ndiode of the production were sent to irradiation for a short time period.\n\nFigure 4.22 show the leakage current measured in the diode after irradiation. As measu-\n\nrement were taken at room temperature, very high leakage current value can be observed.\n\nMeasurement were performed very quickly to avoid the heating of the diode that was also\n\npromptly put back to its cold storage. No apparent breakdown can be observed in any of\n\nthe diode. A slow rise of current, also observed in the current-voltage curve simulated in\n\nchapter 3. The breakdown present before irradiation are quenched by the presence of the\n\nhigh amount of defects trapping the charge before it can multiply and create a cascade\n\nleaking to breakdown. Leakage value for the different diodes irradiated at the same fluence\n\nis uniform showing that leakage current after irradiation is dominated by bulk defect ge-\n\nneration recombination current and not surface quality and edge effects as usually seen\n\nbefore irradiation.\n\nExperimental measurements presented in this section were used to better understand\n\nthe behavior of the sensors and interpretation of the simulation results can be used to\n\ngain understanding of the experimental data. Guard ring potential are well reproduced\n\n145\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) n-in-n production\n\n(b) n-in-p production\n\nFigure 4.21 – Leakage current in the guard ring diodes of the PPSU09 production\n\n146\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\n(a) n-in-n production\n\n(b) n-in-p production\n\nFigure 4.22 – Leakage current in the guard ring diodes of the PPSU09 production after\nirradiation\n\n147\n\n\n\n4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION\n\nby simulation, both before and after irradiation, in the case studied here, and lead us\n\nto be confident in the results coming from TCAD simulation. The use of experimental\n\ndoping profile allow easy plug-and-play reproduction of the guard ring structure behavior.\n\nThe model presented in this work can be used for optimization of guard ring structure for\n\nfuture sensor such as the super LHC pixel detector. Leakage current and breakdown voltage\n\nremain hard to predict but the soft breakdown observed in radiation damage simulation is\n\nalso observed in irradiated devices.The breakdown voltage value predicted by the simulation\n\nseems to be higher than the one observed in reality. The qualitative tendency can however\n\nbe used to gain insight on the effect of modifications to the design of multi-guard ring\n\nstructure.\n\n148\n\n\n\nChapitre 5\n\nPlanar Pixel Sensor digitization for\nATLAS IBL simulation\n\nThe ATLAS IBL pixel sensor will be subject to extensive radiation damage that will\n\naffect its performance in terms of reconstruction of the charged particles trajectory and\n\nimpulsion. Charge collection efficiency will be reduced by trapping and high electric field\n\nwill be present in the bulk of the sensor. Space charge inversion will eventually occur\n\nand sensor will futher need to be operated underdepleted as bias voltage sufficient for full\n\ndepletion will not be reachable by the power supply of the detector system. To produce\n\naccurate simulation of the full detector system and evaluate reconstruction performances,\n\nwe need an accurate and fast model of the charge collection to digitize the energy deposition\n\ninformation coming from the GEANT4 simulation of the detector. Our group has developed\n\na digitizer based on our knowledge of TCAD and Monte-Carlo simulation of pixel detectors.\n\nA simulation framework , the ALLPix software, was developed. It provides an easy\n\ntest-bench for digitizer using the GEANT4 [51] simulation package. The software can be\n\nused to simulate any pixel sensor geometry along with its surrounding environment. A\n\nrealistic model of the EUDET pixel telescope [52], shown in figure 5.1, was used in the\n\ntest beam period of November 2009, july and november 2010 at CERN SPS (120 GeV\n\npions) and in May 2010 at DESY (1-4 GeV electrons). It provides a simulation tool of\n\nthe telescope that is useful for debugging and understanding the behavior of the telescope.\n\nVarious digitization model can be used to convert the GEANT4 hits of the device under\n\ntest into detector hit information. The simulation can be used to generate virtual telescope\n\n149\n\n\n\ndata that can be reconstructed using the telescope reconstruction software. The comparison\n\nbetween the real test beam data and simulated one will be used to validate the digitizer\n\npackage developed at LAL. Once validated. this digitizer can then be transferred to the\n\nATLAS simulation software to perform full detector simulation.\n\nFigure 5.1 – EUDET telescope geometry implemented in the ALLPix framework. Wire\nframe box represent the 6 telescope planes and the green volumes in the middle are the\nDUT PCB card\n\nThe LAL digitization model presented here aims to provide a simple digitization tool\n\nfor the FE-I3 and FE-I4 planar pixel sensor featuring the slim edges guard ring structures\n\nproposed for the IBL pixel detector. The radiation damage model used to compute the\n\ntrapping time and resistivity of the bulk after exposure to radiation is the Hamburg model\n\npresented in chapter 2.\n\nTo simulate charge transport in the bulk of the pixel sensor, we first take the trajectory\n\nof the particle crossing the sensitive volume and divide it into a smaller fraction of charge\n\n150\n\n\n\ndeposition hits. The energy deposition of each hit is translated in a number of electron hole\n\npair and variation of this number following the Poisson statistics and the Fano factor is\n\ntaken into account. The number of these hit can be set as a free parameter to allow more or\n\nless accurate representation of the charge deposition along the track. The punctual charge\n\nelement are then transported in the electric following the drift-diffusion equation. Effects\n\nsuch as saturation velocity are taken into account to accurately compute the drift time of\n\nthe punctual charge element. Electric field can be provided through a TCAD simulation\n\nresult of evaluated using an analytical function. The integration of the trajectory is done\n\nusing a fifth order integration method called Runge-Kutta-Fehlberg method [53]. For most\n\nsimulation purpose, the drift inside unidimensional electric field should be sufficient to\n\nreproduce well the experimental data. A schematic of the digitization charge transport\n\nmodel can be seen in figure 5.2, where the drift cone of the charge element are shown\n\nspreading from the track position along the drift trajectory.\n\nFigure 5.2 – Schematic of the digitization model\n\nA lifetime is also computed for each charge element hole and electrons using the trapping\n\nlifetime computed with the Hamburg model. The charge element trapping position in the\n\nsensor is determined using the drift trajectory and velocity of each charge element. Using\n\nthe initial and final position of the charge element, we can use Ramo theorem (equation\n\nThe lateral diffusion associated to each charge element is also computed using the\n\ndrift time of the charge element and the classical solution of the diffusion equation for a\n\n151\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\ngaussian distribution. Equation 5.1 describe the evolution of the standard deviation of the\n\ndistribution with time in silicon. D is the diffusion coefficient.\n\nσx,y,z(t) =\n√\n\n2De,ht (5.1)\n\nThe resulting gaussian distribution of charge is then projected on the pixel plane, and\n\ncharge inside the element is divided between the pixels using equation 5.2\n\nQi =\n\n∫ xf i\n\nx0i\n\n∫ yf i\n\ny0i\nρ(x, y)dxdy (5.2)\n\nwhere Qi is the charge collected by electrode i, bounded by the rectangle extending\n\nfrom x0i to Xf i and y0i to yf i and ρ(x, y) the projected gaussian distribution of the\n\ncharge element on the pixel surface. Finally, the threshold of the readout chip threshold\n\nis simulated by eliminating pixel hit under a constant threshold that is a free parameter\n\nof the model. This model was implemented in a digitizer for the allpix framework. Results\n\nand comparison with test beam data will be shown in the next section.\n\n5.1 Test beam validation of TCAD simulation and digitiza-\ntion\n\nThe EUDET telescope has been used to study the tracking performances of the different\n\nflavor of ATLAS pixel sensor from the PPSU production. The telescope is composed of 6\n\nplanes of MIMOSA26 MAPS sensor with a pitch of 18.5 µm arranged in a matrix of\n\n1156x1156 pixels. The telescope is divided in two arms located each side of the ATLAS pixel\n\ndetector assemblies. Particles crossing the entire telescope were selected using 4 scintillator\n\ntriggers in coincidence two by two on each end of the telescope assembly. Data taken by\n\nthe ATLAS pixel assemblies are recorded for the 16 next level 1 trigger count. The EUDET\n\nsoftware is then used to reconstruct the trajectories of the recorded particles. The tracks\n\nare extrapolated to the device under test and used to analyze the behavior of the ATLAS\n\npixel sensor. Track positioning resolution between the telescope arms is 3 µm.\n\n152\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\n5.1.1 Validation of the digitization model\n\nI will present here a validation of the digitizer using the reconstructed data from CERN\n\nplanar pixel October 2010 test beam period. The devices under test that were evaluated\n\nwere , in increasing order distance from the beam : a 11 guard ring ATLAS n-in-n sensor\n\nfrom the PPSU09 production, a n-in-n stepwise shifted pixel structure and a n-in-p unir-\n\nradiated standard small guard ring pixel detector also from the PPSU09. A fourth sensor\n\nfrom Hammamatsu was present in the beam but it not considered in this analysis. The data\n\ncontained 6.89 million events taken from run 20275 to 20358. Bias voltage were maintained\n\nat 150 V through the run and sensor were operated at room temperature. A simulation of\n\n500 000 trigger event were accumulated using the telescope geometry to compare with the\n\ndata from the pixel sensor under test.\n\nSimulation was performed for a 250 µm thick 5000 kΩcm−1 sensor biased at 150 V.\n\nElectric field was considered from a TCAD simulation of a single pixel cell for a cutline\n\ntaken at its middle through the depth of the model. The diffusion coefficient for electrons\n\nwas 40.2 cm2/s and mobility was computed using the field dependent model shown in\n\nchapter 2.\n\nFigure 5.3 and 5.4 show the unbiased residual of the track position reconstructed with\n\nthe telescope with respect to the reconstructed hit position in the devices under test.\n\nThree methods of hit position reconstruction are used and compared in the test beam data\n\nreconstruction. The digital and analog method consist respectively of computing the center\n\nof gravity of the hit pixel that are regrouped in a cluster and calculating the charge-weighted\n\ncenter of gravity of the cluster’s pixel. The simulation data were reconstructed in the same\n\nmanner and figure ?? and ?? show the residual distribution between the Monte-Carlo truth\n\nparticle hit position and the reconstructed position from digitization data.\n\nCluster size distribution of the device under test can be seen in figure 5.6 along with the\n\nsimulated data. The simulation reproduce well within 3.5% the size distribution of clusters\n\nin test beam data for all three sensors. The Time-Over-Threshold (TOT) distribution in\n\nthe pixel X direction can be seen in figure ?? for the three devices tested and for the\n\nsimulation of the devices along with the results from simulation.\n\n153\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\n(a) X direction (400 µm pitch)\n\n(b) Y direction (50 µm pitch)\n\nFigure 5.3 – Experimental unbiased residual distribution for the planar sensors between\nthe arms of the telescope, for cluster size 1\n\n154\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\n(a) X, charge weighted mean (b) X, geometrical mean (c) X, Max TOT\n\n(d) Y, charge weighted mean (e) Y, geometrical mean (f) Y, Max TOT\n\nFigure 5.4 – Experimental unbiased residual distribution for the planar sensors between\nthe arms of the telescope, for cluster size 2\n\n(a) X, charge weighted mean (b) X, geometrical mean (c) X,Max TOT\n\n(d) Y, charge weighted mean (e) Y, geometrical mean (f) y Max TOT\n\nFigure 5.5 – Simulated unbiased residual distribution for the planar sensors between the\narms of the telescope\n\n155\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\nFigure 5.6 – Cluster size distribution in experimental Pion test beam data compared to\ndigitization\n\n(a) 15 GR n-in-n (b) stepwise structure\n\n(c) n-in-p small guard rings (d) Simulation\n\nFigure 5.7 – TOT Profile in the X pixel direction, average of all pixels\n\n156\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\nThe simulation results are in good agreement with the results obtained with the three\n\ndevice in test beam. The Charge profile along the X direction are different for the expe-\n\nrimental dara compared to simulated data because of the punch-trough structure, seen in\n\nfigure 5.8, which is used to bias the pixel and collect charges so that it does not reach\n\nthe readout chip. This mechanism is not included in the simulation. The TOT average\n\nvalue from simulation is also lower than in the experimental data because the sensor was\n\nsimulated with a thinner bulk. The calibration of the virtual chip was kept to obtain a\n\nvalue of 60 for a deposited charge of 22000 electrons.\n\nFigure 5.8 – FE-I3 standard pixel with its punch-trough bias structure\n\n5.1.2 Edge effects\n\nThe stepwise ATLAS pixel structure has been designed to study the edge effects of the\n\nslim edge structure and verify the predictions of the TCAD simulation of the structure.\n\nFigure 5.9 show the geometry of edge pixel that was used in the production. Edge pixels are\n\nshifted by groups of 8 by steps of 25 microns under the guard ring structure. The efficiency\n\nand charge collection averaged over each set of 8 pixels can be seen in figure 5.10.\n\nFigure 5.9 – GDS drawing of the stepwise pixel structure\n\nThe charge collected under the guard rings is reduced with respect to the charge collec-\n\nted within the guard ring structure. This effect was predicted from TCAD simulation and\n\n157\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\n(a) Detection efficiency at the edge of the sensor\n\n(b) Charge collection profile along a pixel shifted by 200\n\n(c) Charge collection profile along the shifted pixels\n\nFigure 5.10 – Experimental measurement of the charge collection and detection efficiency\nof the slim edge guard ring structure\n\n158\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\ndemonstrated in test beam situation . The detection efficiency is maintained as signal is\n\nlarge enough to trigger the readout electronics. Post-irradiation behavior, with guard rings\n\nloosing their usefulness and SCSI occurring, the difference in charge collection in the active\n\nzone and under the guard ring should be minimal. These data can be used to parametrize\n\nthe charge collection at the edge pixels in the digitizer to simulate the slim edge structure.\n\nOverall , the digitization model reproduce well the behavior of ATLAS FE-I3 unir-\n\nradiated and irradiated sensor tested at CERN using the EUDET telescope. This model\n\ncan be scaled to simulate FE-I4 data and could be useful for future beam test using pixel\n\nsensors. The digitizer that was developed can be used in the general ATLAS simulation\n\nsoftware to take into account effects such parameters like thickness of the sensor and pixel\n\npitch size. This will be used in the IBL ATLAS detector simulation software to simulate\n\nthe slim edge planar sensor candidate for its sensor. The possibility to use TCAD electric\n\nfield and Ramo Potential , including the mechanism of trapping in the simulation is an\n\nideal tool for a detailed simulation of irradiated sensors and should be used to evaluate the\n\nperformances of the ATLAS pixel detector after irradiation .\n\n159\n\n\n\n5.1. TEST BEAM VALIDATION OF TCAD SIMULATION AND DIGITIZATION\n\n160\n\n\n\nConclusion\n\n161\n\n\n\n\n\nCONCLUSION\n\nTCAD simulation have been used for a long time in various engineering field to study\n\ncomplex system and help with their design. The fast computer available today are now\n\nallowing the simulation of large structure using complex model to represent the physics\n\nimplicated in the operation of planar pixel sensor and other semiconductor detection de-\n\nvices. In this work, I presented various simulation results on planar pixel sensor under\n\nvarious condition of operation. It has been shown that reduction of inactive area in pla-\n\nnar pixel sensors can be achieve through the reduction of the number of guard rings and\n\nby using the slim edge structure. The thinning of sensors have been shown to be a valid\n\nmethod to obtain a depleted sensor at lower bias voltage while maintaining comparable\n\nbreakdown voltage associated to thick sensor featuring‘ the same number of guard rings.\n\nCutting edge width reduction have been shown to not influence the lateral depletion of the\n\nsensor. Finally, anomalous charge collection observed in highly irradiated silicon sensor has\n\nbeen explained to first principles using TCAD simulation\n\nThe comparison between experimental data and TCAD simulation has demonstrated\n\nthat TCAD simulation can lead to quantitative results when tuned with the correct process\n\nparameters using the adequate physical models. Further work will be needed to obtain more\n\nquantitative results concerning breakdown voltage for unirradiated and irradiated silicon\n\nsensors. Qualitatively, results obtained here are however encouraging and further work\n\nwill certainly lead to more reasonable results in the near future. Behavior of irradiated\n\nsensors has been reproduced in many aspects, from charge collection and change in bulk\n\nresistivity to double junction formation and failure of the guard ring structures. Further\n\nirradiation studies with test structures will be needed to improve the prediction power of\n\nTCAD simulation. Lower fluences should be explored to observe the failure point of guard\n\nring structure.\n\nBeam test activities have shown that planar pixel sensor can be operated with reduced\n\ninactive area and deliver the same performance as the actual planar pixel detector used in\n\nATLAS inner detector. The need to study fine effects present in these device brought us to\n\ndevelop a simulation package to study the digitization models proposed for our device and\n\ncompare easily with beam test data taken at CERN SPS and DESY Electron Synchrotron.\n\nA digitization model using the Ramo potential and TCAD electric field simulation was\n\n163\n\n\n\nCONCLUSION\n\ndeveloped to help with the study of the new sensor prototypes and to be used in full scale\n\nATLAS and IBL simulation. Comparison with beam test data show the model reproduce\n\nwell the data for unirradiated sensors with various geometry. The model proposed can\n\nbe used to simulate trapping and thinning effects in irradiated sensors. Comparison with\n\nirradiated data and with FEI4 readout should be done to validate further the digitization\n\nmodel.\n\nThe future of silicon detector will be exciting. The arrival of 3D electronics, which\n\nallow to build four-side buttable device and readout electronics using different process for\n\ndifferent functions ,which I had the privilege to see during my thesis, will change our way\n\nto work with silicon sensors and will allow for faster, more radiation hard detector that\n\nexhibit less inactive zones. The first 3D electronic ASIC, the OMEGAPIX, shown in figure\n\n5.11, was received at LAL and open a new era for HEP experiment. I hope my contribution\n\nto the design of the omegapix-2 will lead to exciting new ASICs for the next generation of\n\npixel sensors.\n\nFigure 5.11 – The omegapix analog and digital tier\n\nPersonally ... Leaving your country, your family and your friends to pursue a career\n\non an other continent is something I never thought I would do. It is a very difficult choice\n\n164\n\n\n\nCONCLUSION\n\nto make but it is one I do not regret. During the last three year and half, I had the pleasure\n\nto work in one of the most amazing laboratory I have been honored to know. I travelled\n\naround the world to such exotic places as Alger, Bumerovska or Knoxville. I was granted a\n\nlot of autonomy and for that I thanks all the people who made this possible. I am not the\n\nsame person today I was when I first left home with my package on my back. I discovered\n\nthe real pleasure there can be in making errors and finding the good way to do things by\n\nmyself. There was a lot of frustration, a huge amount of work that do not always show up in\n\nthis thesis because choice were made and sometime because this work is hard to valorize.\n\nOne of my greatest achievement during this thesis, in my opinion, was to build a clean\n\nroom setup that is competitive and can reproduce results taken in other labs where clean\n\nroom activities have been going on for many years, Unfortunately, sometime experimental\n\nwork does not go as fast as you would like it to go. I hope the students who will take\n\nmy place in this group will appreciate the work behind these setup, which will be working\n\nfrom day one for them, and will make good use of it to further advance the accuracy of\n\nour simulation models.\n\nThe knowledge I accumulated here will certainly follow me in my career and I hope to\n\nbe able to continue it in the exciting field of experimental high energy physics as long as\n\npossible.\n\n165\n\n\n\nCONCLUSION\n\n166\n\n\n\nWord Cloud\n\n167\n\n\n\n\n\nWORD CLOUD\n\n169\n\n\n\nWORD CLOUD\n\n170\n\n\n\nBibliographie\n\n[1] O. S. Brüning, P. Collier, P. Lebrun, S. nd Myers, R. Ostojic, J. Poole, and P. Proud-\n\nlock, LHC Design Report. Geneva : CERN, 2004. 17\n\n[2] G. Aad and al., “Atlas pixel detector electronics and sensors,” J. Isnt., vol. 3, p. 7007,\n\n2008. 20, 21\n\n[3] S. Coellia and the ATLAS Pixel Collaboration, “Mechanics and infrastructure for the\n\natlas pixel detector,” J. 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Fehlberg, “Low-order classical runge-kutta formulas with stepsize control,” NASA\n\nTechnical Report, pp. R–315, 1969. 151\n\n\n\n\n\tIntroduction\n\tThe ATLAS experiment and upgrade project \n\tThe Large Hadron Collider\n\tThe ATLAS experiment\n\tThe Inner detector\n\tThe calorimeter\n\tThe muon spectrometer\n\n\tTHe ATLAS upgrade projects\n\tPhase 1 : The Insertable B-Layer (IBL)\n\tPhase 2 : Upgrade for high luminosity\n\n\n\tPrinciples of Silicon pixel sensors \n\tThe physics of Silicon\n\tSemiconductors properties\n\tCharge transport\n\tThe pn junction\n\tPhysical models\n\n\tRadiation detection\n\tThe energy deposition process\n\tSignal formation\n\n\tThe Hybrid Planar Pixel Sensor\n\tOther Silicon sensors\n\tthe 3D pixel sensor\n\tHigh Resistivity Monolithic Active Pixel Sensors (MAPS)\n\n\tRadiation damage in Silicon sensors\n\tNon-ionizing Energy Loss (NIEL)\n\tIonizing energy loss\n\n\n\tTCAD Simulation models \n\tProcess simulation\n\tDevice simulation\n\tGeometry\n\tboundary conditions\n\n\tThe Multi-Guard Ring structure\n\tPrinciples of guard ring structures\n\tOptimization of guard ring structures for reduction of inactive area and radiation hardness \n\tThe Slim Edge Guard Ring structure\n\n\tThe charge amplification mechanism in highly irradiated silicon sensors\n\n\tFrom TCAD simulation to experimental data\n\tExperimental validation of TCAD simulation\n\tDoping profile measurements\n\tGuard Ring measurements\n\tCurrent versus Bias characteristics\n\n\n\tPlanar Pixel Sensor digitization for ATLAS IBL simulation\n\tTest beam validation of TCAD simulation and digitization\n\tValidation of the digitization model\n\tEdge effects\n\n\n\tConclusion\n\tWord Cloud\n\tBibliography"},"fulltext":true,"key":"c56fd24ba5eaaa333c2759245c075b77","url":"https://inspirehep.net/files/c56fd24ba5eaaa333c2759245c075b77"},{"filename":"VA_BENOIT_MATHIEU_10062011.pdf","key":"ee6767ebdc120b7c440d480d25024424","url":"https://inspirehep.net/files/ee6767ebdc120b7c440d480d25024424"}],"$schema":"https://inspirehep.net/schemas/records/hep.json","keywords":[{"source":"author","value":"Radiation Damage"},{"source":"author","value":"IBL"},{"source":"author","value":"SLHC"},{"source":"author","value":"Test Beam"},{"source":"author","value":"Slim edges"},{"source":"author","value":"Planar Pixel Sensor"},{"source":"author","value":"Digitization"},{"source":"author","value":"TCAD simulation"},{"source":"author","value":"Silicon detector"},{"source":"author","value":"-Guard Ring Structure"},{"source":"author","value":"Dommage induit par la radiation"},{"source":"author","value":"Silicium"},{"source":"author","value":"Détecteur pixel planaires"},{"source":"author","value":"Simulation TCAD"},{"source":"author","value":"Test faisceau"},{"source":"author","value":"Numérisation"},{"source":"author","value":"SLHC;"},{"schema":"INSPIRE","value":"thesis"},{"schema":"INSPIRE","value":"semiconductor detector: pixel"},{"schema":"INSPIRE","value":"radiation: damage"},{"schema":"INSPIRE","value":"charge: yield"},{"schema":"INSPIRE","value":"detector: design"},{"schema":"INSPIRE","value":"model"},{"schema":"INSPIRE","value":"ATLAS"},{"schema":"INSPIRE","value":"numerical calculations"}],"references":[{"reference":{"label":"3","misc":["The,. Atlas, and. Projects, Figure 1.21 ? ATLAS super-LHC inner detector planned z ? yi layout view 4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION (a) n-in-n,1 × 10 15 neqcm ?1 (b) n-in-p"]},"raw_refs":[{"schema":"text","value":"3. The, . Atlas, and . Projects, Figure 1.21 ? ATLAS super-LHC inner detector planned z ? yi layout view 4.1. EXPERIMENTAL VALIDATION OF TCAD SIMULATION (a) n-in-n,1 × 10 15 neqcm ?1 (b) n-in-p"}]},{"reference":{"dois":["10.1088/1748-0221/3/07/P07007"],"publication_info":{"year":2008},"misc":["ATLAS pixel detector electronics and sensors, Journal of Instrumentation, vol.3, issue.07, pp.7007-7028",":"],"authors":[{"full_name":"Aad, G."}]},"raw_refs":[{"schema":"text","value":"G. 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Different physical models available have been studied to develop a coherent model of radiation damage in silicon that can be used to predict silicon pixel sensor behavior after exposure to radiation. The Multi-Guard Ring Structure,a protection structure used in pixel sensor design was studied to obtain guidelines for the reduction of inactive edges detrimental to detector operation while keeping a good sensor behavior through its lifetime in the ATLAS detector. A campaign of measurement of the sensor's process parameters and electrical behavior to validate and calibrate the TCAD simulation models and results are also presented. A model for diode charge collection in highly irradiated environment was developed to explain the high charge collection observed in highly irradiated devices. A simple planar pixel sensor digitization model to be used in test beam and full detector system is detailed. It allows for easy comparison between experimental data and prediction by the various radiation damage models available. The digitizer has been validated using test beam data for unirradiated sensors and can be used to produce the first full scale simulation of the ATLAS detector with the IBL that include sensor effects such as slim edge and thinning of the sensor."},{"source":"TEL","value":"In this work, is presented a study, using TCAD simulation, of the possible methods of designing of a planar pixel sensors by reducing their inactive area and improving their radiation hardness for use in the Insertable B-Layer (IBL) project and for SLHC upgrade phase for the ATLAS experiment. Different physical models available have been studied to develop a coherent model of radiation damage in silicon that can be used to predict silicon pixel sensor behavior after exposure to radiation. The Multi-Guard Ring Structure,a protection structure used in pixel sensor design was studied to obtain guidelines for the reduction of inactive edges detrimental to detector operation while keeping a good sensor behavior through its lifetime in the ATLAS detector. A campaign of measurement of the sensor’s process parameters and electrical behavior to validate and calibrate the TCAD simulation models and results are also presented. A model for diode charge collection in highly irradiated environment was developed to explain the high charge collection observed in highly irradiated devices. A simple planar pixel sensor digitization model to be used in test beam and full detector system is detailed. It allows for easy comparison between experimental data and prediction by the various radiation damage models available. The digitizer has been validated using test beam data for unirradiated sensors and can be used to produce the first full scale simulation of the ATLAS detector with the IBL that include sensor effects such as slim edge and thinning of the sensor.","abstract_source_suggest":{"input":"TEL"}},{"source":"dart-europe.org","value":"Le Large Hadron Collider (LHC), située au CERN, Genève, produit des collisions de protons accélérés à une énergie de 3.5 TeV depuis le 23 Novembre 2009. L’expérience ATLAS enregistre depuis des données et poursuit sa recherche de nouvelle physique à travers l’analyse de la cinématique des événements issues des collisions. L’augmentation prévue de la luminosité sur la période s’étalant de 2011 2020 apportera de nouveaux défis pour le détecteur qui doivent être considérés pour maintenir les bonnes performance de la configuration actuelle. Le détecteur interne sera le sous-détecteur le plus affecté par l’augmentation de la luminosité qui se traduira par une augmentation des dommages occasionnés par la forte radiation et par la multiplication du nombre de traces associées à chaque croisement de faisceau. Les dommages causés par l’irradiation intense entrainera une perte d’efficacité de détection et une réduction du nombre de canaux actifs. Un intense effort de Recherche et Développement (R&D) est présentement en cours pour concevoir un nouveau détecteur pixel plus tolérant aux radiations et au cumul des événements générant un grand nombre de traces à reconstruire. Un premier projet de mise-à-jour du détecteur interne, nommé Insertable B-Layer (IBL) consiste à ajouter un couche de détection entre le tube à vide du faisceau et la première couche de silicium. Le projet SLHC prévoit de remplacer l’ensemble du détecteur interne par une version améliorée plus tolérante aux radiations et aux cumuls des événements. Dans cet ouvrage, je présente une étude utilisant la simulation technologique assisté par ordinateur (TCAD) portant sur les méthodes de conception des détecteurs pixels planaires permettant de réduire les zones inactives des détecteurs et d’augmenter leurs tolérances aux radiations. Les différents modèles physiques disponible ont étés étudiés pour développer un modèle cohérent capablede prédire le fonctionnement des détecteurs pixels planaires après irradiation. La structure d’anneaux de gardes utilisée dans le détecteur interne actuel a été étudié pour obtenir de l’information sur les possible méthodes permettant de réduire l’étendu de la surface occupée par cette structure tout en conservant un fonctionnement stable tout au long de la vie du détecteur dans l’expérience ATLAS. Une campagne de mesures sur des structures pixels fut organisée pour comparer les résultats obtenue grâce à la simulation avec le comportement des structures réelles. Les paramètres de fabrication ainsi que le comportement électrique ont été mesurés et comparés aux simulations pour valider et calibrer le modèle de simulation TCAD. Un modèle a été développé pour expliquer la collection de charge excessive observée dans les détecteurs planaires en silicium lors de leur exposition a une dose extrême de radiations. Finalement, un modèle simple de digitalisation à utiliser pour la simulation de performances détecteurs pixels individuels exposés à des faisceau de haute énergie ou bien de l’ensemble du détecteur interne est présenté. Ce modèle simple permets la comparaison entre les données obtenue en faisceau test aux modèle de transport de charge inclut dans ladigitalisation. Le dommage dû à la radiation , l’amincissement et l’utilisation de structures à bords minces sont autant de structures dont les effets sur la collecte de charges affectent les performance du détecteur. Le modèle de digititalisation fut validé pour un détecteur non-irradié en comparant les résultats obtenues avec les données acquises en test faisceau de haut énergie. Le modèle validé sera utilisé pour produire la première simulation de l’IBL incluant les effets d’amincissement du substrat, de dommages dûes aux radiations et de structure dotés de bords fins.In this work, is presented a study, using TCAD simulation, of the possible methods of designing of a planar pixel sensors by reducing their inactive area and improving their radiation hardness for use in the Insertable B-Layer (IBL) project and for SLHC upgrade phase for the ATLAS experiment. Different physical models available have been studied to develop a coherent model of radiation damage in silicon that can be used to predict silicon pixel sensor behavior after exposure to radiation. 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The red and blue shaded regions represent the $\\pi^{0}_{rich}$ and $\\gamma_{rich}$ samples, respectively.","source":"arxiv","key":"0122b9cc68cf366b6cf178617d5fa1e3","url":"https://inspirehep.net/files/0122b9cc68cf366b6cf178617d5fa1e3"},{"filename":"pi0_DzT_NS_AS_AuAu_PP_QM15Prel.png","caption":"Left panel: The $z_{T}$ dependence of \\piZro-$h^{\\pm}$ away-side (upper) and near-side (lower) associated charged-hadron yields per trigger for Au+Au at 0-12$\\%$ centrality  (filled symbols) and  p+p (open symbols) collisions in left panel. Right panel: The \\DirPho-$h^{\\pm}$ away-side associated charged-hadron yields per  trigger for Au+Au at 0-12$\\%$ centrality (filled circles) and  p+p (open circles) collisions. 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kenta","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902941"},"value":"Kyoto U."}],"last_name":"Miyahara","uuid":"c027c2bb-4ffd-4900-bccd-e39be987359c","affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/02kpeqv85"}],"full_name":"Miyahara, Kenta","record":{"$ref":"https://inspirehep.net/api/authors/2366551"},"ids":[{"schema":"INSPIRE BAI","value":"K.Miyahara.1"}],"signature_block":"MYARk","first_name":"Kenta","recid":2366551},{"raw_affiliations":[{"value":"Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502, Japan"}],"full_name_unicode_normalized":"hyodo, tetsuo","affiliations":[{"record":{"$ref":"https://inspirehep.net/api/institutions/902943"},"value":"Kyoto U., Yukawa Inst., Kyoto"}],"last_name":"Hyodo","uuid":"44c4ad9c-7ec8-4b81-97aa-dd5a73faa2fd","affiliations_identifiers":[{"schema":"ROR","value":"https://ror.org/02kpeqv85"}],"full_name":"Hyodo, Tetsuo","curated_relation":true,"record":{"$ref":"https://inspirehep.net/api/authors/1005185"},"ids":[{"schema":"ORCID","value":"0000-0002-4145-9817"},{"schema":"INSPIRE BAI","value":"T.Hyodo.1"}],"signature_block":"HADt","first_name":"Tetsuo","recid":1005185}],"citation_count_without_self_citations":3,"publication_info":[{"journal_volume":"17","artid":"073001","conference_record":{"$ref":"https://inspirehep.net/api/conferences/1407885"},"year":2017,"journal_record":{"$ref":"https://inspirehep.net/api/journals/1289282"},"journal_title":"JPS Conf.Proc.","parent_record":{"$ref":"https://inspirehep.net/api/literature/1611394"},"cnum":"C15-09-07.6"}],"documents":[{"filename":"fulltext1408890.pdf","attachment":{"content":"Composite nature of Λ(1405) from the spacial structure of\n\nK̄N system\n\nKenta Miyahara1 and Tetsuo Hyodo2\n\n1Department of Physics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan\n2Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502, Japan\n\nE-mail: miyahara.kenta.62r@st.kyoto-u.ac.jp\n\n(Received December 10, 2015)\n\nWe discuss the spacial structure of the resonance state, Λ(1405), solving the Schrödinger equation\n\nwith the K̄N local potential. The potential is constructed by paying attention to the two points, the\n\nconstraint from the recent experimental data and the reliability in the complex energy region. Using\n\nthe new local potential, we investigate the K̄N spacial structure and obtain the result indicating that\n\nΛ(1405) is the meson-baryon molecular state, that is, the composite state.\n\nKEYWORDS: Λ(1405), K̄N local potential, chiral unitary approach, spacial structure,\n\ncompositeness\n\nMost of hadrons are interpreted to consist of three quarks (baryon) and a quark-antiquark pair\n\n(meson). Aside from the ordinary structure, the existence of the exotic hadrons, such as multi-\n\nquark states and hadronic molecular states, have been studied. A candidate of the exotic hadron is\n\nΛ(1405) [1], which is considered to be the K̄N molecular states [2]. The structure ofΛ(1405) has been\n\ndiscussed in terms of the compositeness from observable quantities [3] and in terms of strangeness\n\nmagnetic form factor from lattice QCD [4]. On the other hand, in this work, we study the structure of\n\nΛ(1405) from the viewpoint of the K̄N spacial structure [5].\n\nIn order to obtain the K̄N wave function, we first construct the K̄N local potential used in the\n\nSchrödinger equation. As a base of the potential construction, we briefly introduce the chiral unitary\n\napproach [6]. In chiral unitary approach, the s-wave meson-baryon scattering amplitude is calculated\n\nby resumming the interaction terms Vi j from chiral perturbation theory, where i denote the meson-\n\nbaryon channel indices. The free parameters to fit the experimental data are the low energy constants\n\nin Vi j and the subtraction constants in meosn-baryon loop function. In the work of Refs. [6], these\n\nparameters are fitted to reproduce the recent precise experimental data by the SIDDHARTA Collab-\n\noration [7], which leads to the significant reduction of the uncertainty of the K̄N amplitude.\n\nBased on chiral unitary approach, we construct the local potential useful to obtain the spacial\n\nstructure, following Ref. [8]. Using the K̄N single-channel interaction Veff\n11\n\nextracted by the Feshbach\n\nprojection of Vi j, the K̄N local potential U can be constructed as\n\nU(r, E) =\ne−r2/b2\n\nπ3/2b3\n\nMN\n\n2(E + MN + mK)\n\nωK + EN\n\nωK EN\n\n[\n\nVeff\n11 (E + MN + mK) + ∆V(E)\n\n]\n\n. (1)\n\nE, EN , and ωK represent the nonrelativistic meson-baryon total energy, the nucleon energy, and the\n\nanti-kaon energy. MN and mK represent the nucleon mass and the kaon mass. Solving the Schödinger\n\nequation with this potential, we can obtain the scattering amplitude from the behavior of the wave\n\nfunction at the sufficiently large distance. Comparing this amplitude and the original amplitude from\n\nchiral unitary approach, we determined the range parameter b and the correction ∆V . As a charac-\n\nteristic point of this work, we reproduce the original amplitude even in the complex energy plane,\n\nbecause the resonance pole is in the complex energy plane and the spacial structure of the resonance\n\n1\n\nhttp://creativecommons.org/licenses/by/4.0/\nhttp://crossmark.crossref.org/dialog/?doi=10.7566%2FJPSCP.17.073001&domain=pdf&date_stamp=2017-07-18"},"fulltext":true,"key":"86ff39e81e91c9e461ded166e2ae640d","url":"https://inspirehep.net/files/86ff39e81e91c9e461ded166e2ae640d"}],"citeable":true,"$schema":"https://inspirehep.net/schemas/records/hep.json","keywords":[{"source":"author","value":"$\\Lambda(1405)$"},{"source":"author","value":"$\\bar KN$ local potential"},{"source":"author","value":"chiral unitary approach"},{"source":"author","value":"spacial structure"},{"source":"author","value":"compositeness"},{"schema":"INSPIRE","value":"potential: local"},{"schema":"INSPIRE","value":"Lambda(1405)"},{"schema":"INSPIRE","value":"anti-K nucleon"},{"schema":"INSPIRE","value":"structure"},{"schema":"INSPIRE","value":"composite"},{"schema":"INSPIRE","value":"Schroedinger equation"},{"schema":"INSPIRE","value":"meson baryon"}],"references":[{"reference":{"dois":["10.1103/PhysRevLett.2.425;"],"label":"1","publication_info":{"journal_volume":"2","artid":"425","year":1959,"page_start":"425","journal_title":"Phys.Rev.Lett."},"misc":["R.Dalitz and S.Tuan","Additional pubnote: Annals Phys.,10,307"],"authors":[{"full_name":"R.Dalitz"},{"full_name":"S.Tuan"}]},"raw_refs":[{"schema":"text","value":"1) R.Dalitz and S.Tuan, Phys. Rev. Lett. 2, 425 (1959) 10.1103/PhysRevLett.2.425; R.Dalitz and S.Tuan, Ann. Phys. 10, 307 (1960). 10.1016/0003-4916(60)90001-4"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/44384"}},{"reference":{"dois":["10.1016/0003-4916(60)90001-4"],"label":"1"},"raw_refs":[{"schema":"text","value":"1) R.Dalitz and S.Tuan, Phys. Rev. Lett. 2, 425 (1959) 10.1103/PhysRevLett.2.425; R.Dalitz and S.Tuan, Ann. Phys. 10, 307 (1960). 10.1016/0003-4916(60)90001-4"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/44389"}},{"reference":{"dois":["10.1016/j.ppnp.2011.07.002"],"label":"2","publication_info":{"journal_volume":"67","artid":"55","year":2012,"page_start":"55","journal_title":"Prog.Part.Nucl.Phys."},"authors":[{"full_name":"T.Hyodo"},{"full_name":"D.Jido"}]},"raw_refs":[{"schema":"text","value":"2) T.Hyodo and D.Jido, Prog. Part. Nucl. Phys. 67, 55 (2012). 10.1016/j.ppnp.2011.07.002"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/897006"}},{"reference":{"arxiv_eprint":"1509.00146","label":"3","authors":[{"full_name":"Y.Kamiya"},{"full_name":"T.Hyodo"}]},"raw_refs":[{"schema":"text","value":"3) Y.Kamiya and T.Hyodo, arXiv:1509.00146."}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/1391312"}},{"reference":{"dois":["10.1103/PhysRevLett.114.132002"],"label":"4","publication_info":{"journal_volume":"114","artid":"132002","year":2015,"journal_title":"Phys.Rev.Lett."},"authors":[{"full_name":"M.Hall, J.M."}]},"raw_refs":[{"schema":"text","value":"4) J. M. M.Hall et al., Phys. Rev. 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A 881, 98 (2012). 10.1016/j.nuclphysa.2012.01.029"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/927436"}},{"reference":{"dois":["10.1016/j.nuclphysa.2012.01.029"],"label":"6"},"raw_refs":[{"schema":"text","value":"6) Y.Ikeda, T.Hyodo, and W.Weise, Phys. Lett. B 706, 63 (2011) 10.1016/j.physletb.2011.10.068; Y.Ikeda, T.Hyodo, and W.Weise, Nucl. Phys. A 881, 98 (2012). 10.1016/j.nuclphysa.2012.01.029"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/1086833"}},{"reference":{"dois":["10.1016/j.physletb.2011.09.011;"],"label":"7","publication_info":{"journal_volume":"704","artid":"113","year":2011,"page_start":"113","journal_title":"Phys.Lett.B"},"misc":["M.Bazzi et al","Additional pubnote: Nucl.Phys.,A881,88"],"authors":[{"full_name":"M.Bazzi"}]},"raw_refs":[{"schema":"text","value":"7) M.Bazzi et al., Phys. Lett. B 704, 113 (2011) 10.1016/j.physletb.2011.09.011; M.Bazzi et al., Nucl. Phys. 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C 77, 035204 (2008). 10.1103/PhysRevC.77.035204"}],"curated_relation":false,"record":{"$ref":"https://inspirehep.net/api/literature/770146"}}],"number_of_pages":2,"legacy_creation_date":"2015-12-10","preprint_date":"2015-12-08","author_count":2,"public_notes":[{"source":"arXiv","value":"2 pages, 1 figure"}],"earliest_date":"2015-12-08","facet_author_name":["2366551_Kenta Miyahara","1005185_Tetsuo Hyodo"],"core":true,"license":[{"imposing":"arXiv","url":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/"},{"license":"CC-BY-4.0","imposing":"Physical Society of Japan","url":"https://creativecommons.org/licenses/by/4.0/"}],"_oai":{"sets":["Literature"],"id":"oai:inspirehep.net:1408890","updated":"2023-03-06T22:17:40.895042"},"journal_title_variants":["JPS Conf.Proc.","JPS Conf. 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The potential is constructed by paying attention to the two points, the constraint from the recent experimental data and the reliability in the complex energy region. Using the new local potential, we investigate the \\(\\bar{K}\\)N spacial structure and obtain the result indicating that Λ(1405) is the meson-baryon molecular state, that is, the composite state.","abstract_source_suggest":{"input":"Physical Society of Japan"}},{"source":"arXiv","value":"We discuss the spacial structure of the resonance state, Lambda(1405), solving the Schrodinger equation with the KbarN local potential. The potential is constructed by paying attention to the two points, the constraint from the recent experimental data and the reliability in the complex energy region. Using the new local potential, we investigate the KbarN spacial structure and obtain the result indicating that Lambda(1405) is the meson-baryon molecular state, that is, the composite state.","abstract_source_suggest":{"input":"arXiv"}}],"primary_arxiv_category":["nucl-th"],"titles":[{"source":"Physical Society of Japan","title":"Composite nature of $\\Lambda(1405)$ from the spacial structure of $\\bar KN$ system"},{"source":"arXiv","title":"Composite nature of Lambda(1405) from the spacial structure of KbarN system"}],"imprints":[{"date":"2017"}],"curated":true}}],"total":1900569},"links":{"self":"https://inspirehep.net/api/literature/?q=&size=10&page=1","next":"https://inspirehep.net/api/literature/?q=&size=10&page=2","bibtex":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=bibtex","latex-eu":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=latex-eu","latex-us":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=latex-us","json":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=json","json-expanded":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=json-expanded","cv":"https://inspirehep.net/api/literature/?q=&size=10&page=1&format=cv"}}